{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/JohnVKane/Desktop/Analyze Attentive R&R/Replication Files/PSRM STATA Replication Files/PSRM_Replication_Stata_Analyses.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 6 Dec 2022, 23:48:33
{txt}
{com}. 
. *Change font in graphs
. graph set window fontface "ArialNarrow-Bold" // change font
{txt}
{com}. 
. 
. ***************
. ***************
. *NORC Data
. ***************
. ***************
. 
. use "NORC_replicationdata.dta", clear
{txt}(COMMAND CENTER PROJECT : 1149)

{com}. 
. reg NORC_DV i.NORC_Treatment // Experiment ITT replication 

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,021
{txt}{hline 13}{c +}{hline 34}   F(1, 1019)      = {res}    25.41
{txt}       Model {c |} {res} 3.38504411         1  3.38504411   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 135.754741     1,019  .133223494   {txt}R-squared       ={res}    0.0243
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0234
{txt}       Total {c |} {res} 139.139785     1,020  .136411554   {txt}Root MSE        =   {res}   .365

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       NORC_DV{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
NORC_Treatment {c |}
{space 4}Treatment  {c |}{col 16}{res}{space 2}-.1152351{col 28}{space 2} .0228609{col 39}{space 1}   -5.04{col 48}{space 3}0.000{col 56}{space 4}-.1600948{col 69}{space 3}-.0703753
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .5934959{col 28}{space 2} .0164554{col 39}{space 1}   36.07{col 48}{space 3}0.000{col 56}{space 4} .5612056{col 69}{space 3} .6257862
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Figure 2 
. 
. reg NORC_DV i.NORC_Treatment##c.MVC_scale // Interaction model for CATE estimates

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       744
{txt}{hline 13}{c +}{hline 34}   F(3, 740)       = {res}     9.43
{txt}       Model {c |} {res}  3.7488668         3  1.24962227   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 98.0705761       740  .132527806   {txt}R-squared       ={res}    0.0368
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0329
{txt}       Total {c |} {res} 101.819443       743  .137038281   {txt}Root MSE        =   {res} .36404

{txt}{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                   NORC_DV{col 28}{c |} Coefficient{col 40}  Std. err.{col 52}      t{col 60}   P>|t|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}NORC_Treatment {c |}
{space 16}Treatment  {c |}{col 28}{res}{space 2}-.0379878{col 40}{space 2} .0538977{col 51}{space 1}   -0.70{col 60}{space 3}0.481{col 68}{space 4}-.1437985{col 81}{space 3} .0678229
{txt}{space 17}MVC_scale {c |}{col 28}{res}{space 2}-.0028106{col 40}{space 2} .0205879{col 51}{space 1}   -0.14{col 60}{space 3}0.891{col 68}{space 4}-.0432283{col 81}{space 3} .0376071
{txt}{space 26} {c |}
NORC_Treatment#c.MVC_scale {c |}
{space 16}Treatment  {c |}{col 28}{res}{space 2}-.0503021{col 40}{space 2} .0287265{col 51}{space 1}   -1.75{col 60}{space 3}0.080{col 68}{space 4}-.1066973{col 81}{space 3} .0060931
{txt}{space 26} {c |}
{space 21}_cons {c |}{col 28}{res}{space 2} .6091121{col 40}{space 2} .0376503{col 51}{space 1}   16.18{col 60}{space 3}0.000{col 68}{space 4} .5351979{col 81}{space 3} .6830262
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, dydx(NORC_Treatment) at(MVC_scale=(0(1)3)) // generate estimates for figure
{res}
{txt}{col 1}Conditional marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:744}
{txt}{col 1}Model VCE: {res:OLS}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.NORC_Treatment}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 9:MVC_scale} = {res:{ralign 1:0}}
{lalign 7:2._at: }{space 0}{lalign 9:MVC_scale} = {res:{ralign 1:1}}
{lalign 7:3._at: }{space 0}{lalign 9:MVC_scale} = {res:{ralign 1:2}}
{lalign 7:4._at: }{space 0}{lalign 9:MVC_scale} = {res:{ralign 1:3}}

{res}{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31} Delta-method
{col 19}{c |}      dy/dx{col 31}   std. err.{col 43}      t{col 51}   P>|t|{col 59}     [95% con{col 72}f. interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.NORC_Treatment {col 19}{txt}{c |}  (base outcome)
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.NORC_Treatment  {txt}{c |}
{space 14}_at {c |}
{space 15}1  {c |}{col 19}{res}{space 2}-.0379878{col 31}{space 2} .0538977{col 42}{space 1}   -0.70{col 51}{space 3}0.481{col 59}{space 4}-.1437985{col 72}{space 3} .0678229
{txt}{space 15}2  {c |}{col 19}{res}{space 2}-.0882899{col 31}{space 2} .0322767{col 42}{space 1}   -2.74{col 51}{space 3}0.006{col 59}{space 4}-.1516547{col 72}{space 3} -.024925
{txt}{space 15}3  {c |}{col 19}{res}{space 2}-.1385919{col 31}{space 2} .0287929{col 42}{space 1}   -4.81{col 51}{space 3}0.000{col 59}{space 4}-.1951175{col 72}{space 3}-.0820663
{txt}{space 15}4  {c |}{col 19}{res}{space 2} -.188894{col 31}{space 2}   .04761{col 42}{space 1}   -3.97{col 51}{space 3}0.000{col 59}{space 4}-.2823606{col 72}{space 3}-.0954273
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 83}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. 
. *Generating Figure 2
. *Note:  Minor edits were manually done to the graph
. marginsplot, scheme(538bw) legend(ring(0) pos(1))  ///
> xlabel(,grid glpattern(solid) glcolor(gs14)) ///
> ylabel(,grid glpattern(solid) glcolor(gs14)) ///
> xmtick(##2, ticks grid glpattern(solid) glcolor(gs14))  ///
> ymtick(##2, ticks grid glpattern(solid) glcolor(gs14)) ///
> title("") ytitle("{c -(}Effect of Treatment on Support for SL Forgiveness") ///
> xtitle("Number of Correct Mock Vignette Checks (MVCs)") ///
> plotopts(lcolor(black) lwidth(medium) mcolor(black) msymbol(circle) msize(medlarge)) ///
> ciopts(lcolor(black)) recastci(rspike) ///
> addplot(hist MVC_scale, discrete title( " ") ///
> gap(30) ///
> ylabel(-.3(.1).1) ///
> ytitle("Conditional Effect of Student Loan Treatment" "on Support for Loan Forgiveness") ///
> yaxis(2) yscale(alt axis(2)) percent ///
> ytick(-.3(.05).1) ///
> ylabel(0 "0%" 5 "5%" 10 "10%" 15 "15%" 20 "20%" 25 "25%" ///
> 30 "30%" 35 "35%" 40 "40%", labcolor(black*.9) axis(2)) ///
> ytitle("Percent of Sample", axis(2) orientation(rvertical))  ///
> fcolor(gs12%40) fintensity(100) lcolor(none)) ///
> yscale(titlegap(-5) outergap(0)) ///
> legend(off) ///
> xsize(6.5) ysize(3.8)  graphregion(margin(1 2 2 2)) //
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:MVC_scale}{p_end}
{res}{txt}
{com}. 
. *Export graph
. graph export "Fig2a.pdf", as(pdf) replace // create Figure 2a (top panel)
{txt}{p 0 4 2}
file {bf}
/Users/JohnVKane/Desktop/Analyze Attentive R&R/Replication Files/PSRM STATA Replication Files/Fig2a.pdf{rm}
saved as
PDF
format
{p_end}

{com}. 
. *Performance on the MVC Scale
. tab MVC_scale

  {txt}MVC_scale {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}         96       12.87       12.87
{txt}          1 {c |}{res}        222       29.76       42.63
{txt}          2 {c |}{res}        287       38.47       81.10
{txt}          3 {c |}{res}        141       18.90      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        746      100.00
{txt}
{com}. 
. *Pairwise correlations between MVCs
. pwcorr MVC1 MVC2 MVC3, sig

             {txt}{c |}     MVC1     MVC2     MVC3
{hline 13}{c +}{hline 27}
        MVC1 {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
        MVC2 {c |} {res}  0.1351   1.0000 
             {txt}{c |}{res}   0.0002
             {txt}{c |}
        MVC3 {c |} {res}  0.3199   0.1141   1.0000 
             {txt}{c |}{res}   0.0000   0.0018
             {txt}{c |}

{com}. 
. *Cronbach's alpha value for MVC scale items
. alpha MVC1 MVC2 MVC3, item

{txt}Test scale = mean(unstandardized items)

                                                            Average
                             Item-test     Item-rest       interitem
Item         {c |}  Obs  Sign   correlation   correlation     covariance      alpha
{hline 13}{c +}{hline 65}
MVC1{col 14}{c |}{res}{col 16} 746{col 24}+{col 31} 0.6648{col 45} 0.3071{col 59} .0273962{col 73} 0.2048
{txt}MVC2{col 14}{c |}{res}{col 16} 746{col 24}+{col 31} 0.6344{col 45} 0.1514{col 59} .0630621{col 73} 0.4748
{txt}MVC3{col 14}{c |}{res}{col 16} 746{col 24}+{col 31} 0.7300{col 45} 0.2737{col 59} .0256617{col 73} 0.2341
{txt}{hline 13}{c +}{hline 65}
Test scale{col 14}{c |}{res}{col 59} .0387067{col 73} 0.4011
{txt}{hline 13}{c BT}{hline 65}

{com}. 
. 
. ********************
. ** Table A1 Analyses
. ********************
. ci means MVdisplaytime // average time on screen

{txt}    Variable {c |}        Obs        Mean    Std. err.       [95% conf. interval]
{hline 13}{c +}{hline 63}
MVdisplayt~e {c |}{col 16}{res}       746{col 29} 38.13137{col 41} 2.315738{col 57} 33.58522{col 69} 42.67752{txt}

{com}. proportion MVC1 MVC2 MVC3 // proportion passing each MVC
{res}
{txt}{col 1}Proportion estimation{col 44}{lalign 13:Number of obs}{col 57} = {res}{ralign 3:746}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 37}             L{col 51}ogit
{col 14}{c |} Proportion{col 26}   Std. err.{col 38}     [95% con{col 51}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 8}MVC1 {c |}
{space 2}Incorrect  {c |}{col 14}{res}{space 2} .1930295{col 26}{space 2} .0144501{col 37}{space 5} .1662337{col 51}{space 3} .2229894
{txt}{space 4}Correct  {c |}{col 14}{res}{space 2} .8069705{col 26}{space 2} .0144501{col 37}{space 5} .7770106{col 51}{space 3} .8337663
{txt}{space 12} {c |}
{space 8}MVC2 {c |}
{space 2}Incorrect  {c |}{col 14}{res}{space 2} .6380697{col 26}{space 2} .0175945{col 37}{space 5} .6028676{col 51}{space 3} .6718499
{txt}{space 4}Correct  {c |}{col 14}{res}{space 2} .3619303{col 26}{space 2} .0175945{col 37}{space 5} .3281501{col 51}{space 3} .3971324
{txt}{space 12} {c |}
{space 8}MVC3 {c |}
{space 2}Incorrect  {c |}{col 14}{res}{space 2} .5348525{col 26}{space 2} .0182618{col 37}{space 5} .4988835{col 51}{space 3} .5704628
{txt}{space 4}Correct  {c |}{col 14}{res}{space 2} .4651475{col 26}{space 2} .0182618{col 37}{space 5} .4295372{col 51}{space 3} .5011165
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. 
. 
. di .8069705-.2 // difference b/w proportion passing MVC1 vs. chance
{res}.6069705
{txt}
{com}. prtest MVC1==.2 // significance test for this difference

{txt}One-sample test of proportion                   Number of obs      = {res}      746

{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. err.{col 44}{col 49}{col 59}[95% conf. interval]
{hline 13}{c +}{hline 64}
{col 9}MVC1{col 14}{c |}{res}{col 17} .8069705{col 28} .0144501{col 58} .7786488{col 70} .8352922
{txt}{hline 13}{c BT}{hline 64}
    p = proportion({res}MVC1{txt})                                          z = {res} 41.4455
{txt}H0: p = {res}0.2

     {txt}Ha: p < {res}0.2                 {txt}Ha: p != {res}0.2                   {txt}Ha: p > {res}0.2
 {txt}Pr(Z < z) = {res}1.0000         {txt}Pr(|Z| > |z|) = {res}0.0000          {txt}Pr(Z > z) = {res}0.0000
{txt}
{com}. 
. di .3619303-.2 // difference b/w proportion passing MVC2 vs. chance
{res}.1619303
{txt}
{com}. prtest MVC2==.2 // significance test for this difference

{txt}One-sample test of proportion                   Number of obs      = {res}      746

{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. err.{col 44}{col 49}{col 59}[95% conf. interval]
{hline 13}{c +}{hline 64}
{col 9}MVC2{col 14}{c |}{res}{col 17} .3619303{col 28} .0175945{col 58} .3274457{col 70} .3964149
{txt}{hline 13}{c BT}{hline 64}
    p = proportion({res}MVC2{txt})                                          z = {res} 11.0570
{txt}H0: p = {res}0.2

     {txt}Ha: p < {res}0.2                 {txt}Ha: p != {res}0.2                   {txt}Ha: p > {res}0.2
 {txt}Pr(Z < z) = {res}1.0000         {txt}Pr(|Z| > |z|) = {res}0.0000          {txt}Pr(Z > z) = {res}0.0000
{txt}
{com}. 
. di .4651475-.2 // difference b/w proportion passing MVC2 vs. chance
{res}.2651475
{txt}
{com}. prtest MVC3==.2 // significance test for this difference

{txt}One-sample test of proportion                   Number of obs      = {res}      746

{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. err.{col 44}{col 49}{col 59}[95% conf. interval]
{hline 13}{c +}{hline 64}
{col 9}MVC3{col 14}{c |}{res}{col 17} .4651475{col 28} .0182618{col 58}  .429355{col 70} .5009399
{txt}{hline 13}{c BT}{hline 64}
    p = proportion({res}MVC3{txt})                                          z = {res} 18.1049
{txt}H0: p = {res}0.2

     {txt}Ha: p < {res}0.2                 {txt}Ha: p != {res}0.2                   {txt}Ha: p > {res}0.2
 {txt}Pr(Z < z) = {res}1.0000         {txt}Pr(|Z| > |z|) = {res}0.0000          {txt}Pr(Z > z) = {res}0.0000
{txt}
{com}. 
. ********************
. *Table A7 Analyses
. ********************
. bysort NORC_Treatment:  tab MVC_scale // % passing MVCs by experimental condition

{txt}{hline}
-> NORC_Treatment = Control

  MVC_scale {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}         51       14.05       14.05
{txt}          1 {c |}{res}        113       31.13       45.18
{txt}          2 {c |}{res}        138       38.02       83.20
{txt}          3 {c |}{res}         61       16.80      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        363      100.00

{txt}{hline}
-> NORC_Treatment = Treatment

  MVC_scale {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}         45       11.75       11.75
{txt}          1 {c |}{res}        109       28.46       40.21
{txt}          2 {c |}{res}        149       38.90       79.11
{txt}          3 {c |}{res}         80       20.89      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        383      100.00

{txt}
{com}. tab MVC_scale // overall % passing n number of MVCs

  {txt}MVC_scale {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}         96       12.87       12.87
{txt}          1 {c |}{res}        222       29.76       42.63
{txt}          2 {c |}{res}        287       38.47       81.10
{txt}          3 {c |}{res}        141       18.90      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        746      100.00
{txt}
{com}. 
. ********************
. *Table B1. Demographic Results (restricted to those featured in model)
. ********************
. reg NORC_DV i.NORC_Treatment##c.MVC_scale // model to use for calculating demographics

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       744
{txt}{hline 13}{c +}{hline 34}   F(3, 740)       = {res}     9.43
{txt}       Model {c |} {res}  3.7488668         3  1.24962227   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 98.0705761       740  .132527806   {txt}R-squared       ={res}    0.0368
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0329
{txt}       Total {c |} {res} 101.819443       743  .137038281   {txt}Root MSE        =   {res} .36404

{txt}{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                   NORC_DV{col 28}{c |} Coefficient{col 40}  Std. err.{col 52}      t{col 60}   P>|t|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}NORC_Treatment {c |}
{space 16}Treatment  {c |}{col 28}{res}{space 2}-.0379878{col 40}{space 2} .0538977{col 51}{space 1}   -0.70{col 60}{space 3}0.481{col 68}{space 4}-.1437985{col 81}{space 3} .0678229
{txt}{space 17}MVC_scale {c |}{col 28}{res}{space 2}-.0028106{col 40}{space 2} .0205879{col 51}{space 1}   -0.14{col 60}{space 3}0.891{col 68}{space 4}-.0432283{col 81}{space 3} .0376071
{txt}{space 26} {c |}
NORC_Treatment#c.MVC_scale {c |}
{space 16}Treatment  {c |}{col 28}{res}{space 2}-.0503021{col 40}{space 2} .0287265{col 51}{space 1}   -1.75{col 60}{space 3}0.080{col 68}{space 4}-.1066973{col 81}{space 3} .0060931
{txt}{space 26} {c |}
{space 21}_cons {c |}{col 28}{res}{space 2} .6091121{col 40}{space 2} .0376503{col 51}{space 1}   16.18{col 60}{space 3}0.000{col 68}{space 4} .5351979{col 81}{space 3} .6830262
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. tabstat HH_Income age educ if e(sample), st(mean p50) // descriptive stats for income, age, educ

{txt}   Stats {...}
{c |}{...}
  HH_Inc~e       age      educ
{hline 9}{c +}{hline 30}
{ralign 8:Mean} {...}
{c |}{...}
 {res} 9.676075   48.5672  10.67608
{txt}{ralign 8:p50} {...}
{c |}{...}
 {res}       10        47        10
{txt}{hline 9}{c BT}{hline 30}

{com}. 
. tab gender if e(sample) // % female in sample

 {txt}Respondent {c |}
     gender {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       Male {c |}{res}        363       48.79       48.79
{txt}     Female {c |}{res}        381       51.21      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        744      100.00
{txt}
{com}. 
. tab race_5cat if e(sample) // % of each racial group

       {txt}RECODE of racethnicity {c |}
    (Combined Race/Ethnicity) {c |}      Freq.     Percent        Cum.
{hline 30}{c +}{hline 35}
           Non-Hispanic White {c |}{res}        507       68.15       68.15
{txt}Non-Hispanic African-American {c |}{res}         80       10.75       78.90
{txt}                     Hispanic {c |}{res}        109       14.65       93.55
{txt}                        Asian {c |}{res}         18        2.42       95.97
{txt}                        Other {c |}{res}         30        4.03      100.00
{txt}{hline 30}{c +}{hline 35}
                        Total {c |}{res}        744      100.00
{txt}
{com}. 
. tab partyid if e(sample) // % of partisans

                    {txt}partyid {c |}      Freq.     Percent        Cum.
{hline 28}{c +}{hline 35}
            Strong Democrat {c |}{res}        120       16.22       16.22
{txt}          Moderate Democrat {c |}{res}        130       17.57       33.78
{txt}              Lean Democrat {c |}{res}         89       12.03       45.81
{txt}Don't Lean/Independent/None {c |}{res}        127       17.16       62.97
{txt}            Lean Republican {c |}{res}         93       12.57       75.54
{txt}        Moderate Republican {c |}{res}        107       14.46       90.00
{txt}          Strong Republican {c |}{res}         74       10.00      100.00
{txt}{hline 28}{c +}{hline 35}
                      Total {c |}{res}        740      100.00
{txt}
{com}. 
. * Additional information
. tab HH_Income if e(sample) // distribution of income

  {txt}Household {c |}
     Income {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
        <5k {c |}{res}         19        2.55        2.55
{txt}      5-10k {c |}{res}         21        2.82        5.38
{txt}     10-15k {c |}{res}         31        4.17        9.54
{txt}     15-20k {c |}{res}         35        4.70       14.25
{txt}     20-25k {c |}{res}         46        6.18       20.43
{txt}     25-30k {c |}{res}         40        5.38       25.81
{txt}     30-35k {c |}{res}         39        5.24       31.05
{txt}     35-40k {c |}{res}         31        4.17       35.22
{txt}     40-49k {c |}{res}         62        8.33       43.55
{txt}     50-60k {c |}{res}         75       10.08       53.63
{txt}     60-75k {c |}{res}         92       12.37       65.99
{txt}     75-85k {c |}{res}         32        4.30       70.30
{txt}    85-100k {c |}{res}         67        9.01       79.30
{txt}   100-124k {c |}{res}         66        8.87       88.17
{txt}   125-150k {c |}{res}         40        5.38       93.55
{txt}   150-175k {c |}{res}         23        3.09       96.64
{txt}   175-200k {c |}{res}          3        0.40       97.04
{txt}      >200k {c |}{res}         22        2.96      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        744      100.00
{txt}
{com}. tab HH_Income if e(sample), nol // distribution of income (no labels)

  {txt}Household {c |}
     Income {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}         19        2.55        2.55
{txt}          2 {c |}{res}         21        2.82        5.38
{txt}          3 {c |}{res}         31        4.17        9.54
{txt}          4 {c |}{res}         35        4.70       14.25
{txt}          5 {c |}{res}         46        6.18       20.43
{txt}          6 {c |}{res}         40        5.38       25.81
{txt}          7 {c |}{res}         39        5.24       31.05
{txt}          8 {c |}{res}         31        4.17       35.22
{txt}          9 {c |}{res}         62        8.33       43.55
{txt}         10 {c |}{res}         75       10.08       53.63
{txt}         11 {c |}{res}         92       12.37       65.99
{txt}         12 {c |}{res}         32        4.30       70.30
{txt}         13 {c |}{res}         67        9.01       79.30
{txt}         14 {c |}{res}         66        8.87       88.17
{txt}         15 {c |}{res}         40        5.38       93.55
{txt}         16 {c |}{res}         23        3.09       96.64
{txt}         17 {c |}{res}          3        0.40       97.04
{txt}         18 {c |}{res}         22        2.96      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        744      100.00
{txt}
{com}. 
. tab educ if e(sample) // distribution of education

    {txt}Education (Highest Degree Received) {c |}      Freq.     Percent        Cum.
{hline 40}{c +}{hline 35}
                             10th grade {c |}{res}          6        0.81        0.81
{txt}                             11th grade {c |}{res}          8        1.08        1.88
{txt}                  12th grade NO DIPLOMA {c |}{res}         11        1.48        3.36
{txt}HIGH SCHOOL GRADUATE - high school DIPL {c |}{res}        148       19.89       23.25
{txt}                Some college, no degree {c |}{res}        217       29.17       52.42
{txt}                       Associate degree {c |}{res}        107       14.38       66.80
{txt}                       Bachelors degree {c |}{res}        152       20.43       87.23
{txt}                         Masters degree {c |}{res}         70        9.41       96.64
{txt}       Professional or Doctorate degree {c |}{res}         25        3.36      100.00
{txt}{hline 40}{c +}{hline 35}
                                  Total {c |}{res}        744      100.00
{txt}
{com}. tab educ if e(sample), nol // distribution of education (no labels)

  {txt}Education {c |}
   (Highest {c |}
     Degree {c |}
  Received) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          6 {c |}{res}          6        0.81        0.81
{txt}          7 {c |}{res}          8        1.08        1.88
{txt}          8 {c |}{res}         11        1.48        3.36
{txt}          9 {c |}{res}        148       19.89       23.25
{txt}         10 {c |}{res}        217       29.17       52.42
{txt}         11 {c |}{res}        107       14.38       66.80
{txt}         12 {c |}{res}        152       20.43       87.23
{txt}         13 {c |}{res}         70        9.41       96.64
{txt}         14 {c |}{res}         25        3.36      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        744      100.00
{txt}
{com}. 
. ********************
. *Table D1 Analyses
. ********************
. *Demographic predictors of MVC performance
. reg MVC_scale_01 i.gender i.race_5cat age_01 income_01 educ_01 pid7_01 

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       742
{txt}{hline 13}{c +}{hline 34}   F(9, 732)       = {res}     7.95
{txt}       Model {c |} {res} 6.37142784         9  .707936427   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 65.1866739       732  .089052833   {txt}R-squared       ={res}    0.0890
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0778
{txt}       Total {c |} {res} 71.5581017       741  .096569638   {txt}Root MSE        =   {res} .29842

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}               MVC_scale_01{col 29}{c |} Coefficient{col 41}  Std. err.{col 53}      t{col 61}   P>|t|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}gender {c |}
{space 20}Female  {c |}{col 29}{res}{space 2}  .027564{col 41}{space 2} .0222967{col 52}{space 1}    1.24{col 61}{space 3}0.217{col 69}{space 4}-.0162092{col 82}{space 3} .0713372
{txt}{space 27} {c |}
{space 18}race_5cat {c |}
Non-Hispanic African-Ame..  {c |}{col 29}{res}{space 2}-.1418825{col 41}{space 2} .0375795{col 52}{space 1}   -3.78{col 61}{space 3}0.000{col 69}{space 4} -.215659{col 82}{space 3} -.068106
{txt}{space 18}Hispanic  {c |}{col 29}{res}{space 2}-.1167726{col 41}{space 2} .0327148{col 52}{space 1}   -3.57{col 61}{space 3}0.000{col 69}{space 4}-.1809986{col 82}{space 3}-.0525466
{txt}{space 21}Asian  {c |}{col 29}{res}{space 2} .0531728{col 41}{space 2} .0727174{col 52}{space 1}    0.73{col 61}{space 3}0.465{col 69}{space 4}-.0895868{col 82}{space 3} .1959324
{txt}{space 21}Other  {c |}{col 29}{res}{space 2}-.0060932{col 41}{space 2} .0562132{col 52}{space 1}   -0.11{col 61}{space 3}0.914{col 69}{space 4}-.1164515{col 82}{space 3} .1042651
{txt}{space 27} {c |}
{space 21}age_01 {c |}{col 29}{res}{space 2} .0173813{col 41}{space 2} .0473897{col 52}{space 1}    0.37{col 61}{space 3}0.714{col 69}{space 4}-.0756547{col 82}{space 3} .1104173
{txt}{space 18}income_01 {c |}{col 29}{res}{space 2} .1270085{col 41}{space 2}  .049351{col 52}{space 1}    2.57{col 61}{space 3}0.010{col 69}{space 4} .0301221{col 82}{space 3}  .223895
{txt}{space 20}educ_01 {c |}{col 29}{res}{space 2} .2749801{col 41}{space 2} .0791528{col 52}{space 1}    3.47{col 61}{space 3}0.001{col 69}{space 4} .1195866{col 82}{space 3} .4303736
{txt}{space 20}pid7_01 {c |}{col 29}{res}{space 2}-.0056863{col 41}{space 2} .0351381{col 52}{space 1}   -0.16{col 61}{space 3}0.871{col 69}{space 4}-.0746698{col 82}{space 3} .0632971
{txt}{space 22}_cons {c |}{col 29}{res}{space 2} .3092684{col 41}{space 2}  .060556{col 52}{space 1}    5.11{col 61}{space 3}0.000{col 69}{space 4} .1903844{col 82}{space 3} .4281525
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Produce Table D1 (NORC column)
. outreg2 using TableD1.doc, ctitle(NORC) dec(2) e(r2_a) ///
> alpha(0.001, 0.01, 0.05, .10) symbol(***, **, *,^) 
{txt}{stata `"shellout using `"TableD1.doc"'"':TableD1.doc}
{browse `"/Users/JohnVKane/Desktop/Analyze Attentive R&R/Replication Files/PSRM STATA Replication Files"' :dir}{com} : {txt}{stata `"seeout using "TableD1.txt""':seeout}

{com}. 
. *Correlations between race, age, and MVC performance
. tab race_5cat, gen(race_dummy) // generate a race dummy variable

       {txt}RECODE of racethnicity {c |}
    (Combined Race/Ethnicity) {c |}      Freq.     Percent        Cum.
{hline 30}{c +}{hline 35}
           Non-Hispanic White {c |}{res}        692       67.51       67.51
{txt}Non-Hispanic African-American {c |}{res}        106       10.34       77.85
{txt}                     Hispanic {c |}{res}        153       14.93       92.78
{txt}                        Asian {c |}{res}         27        2.63       95.41
{txt}                        Other {c |}{res}         47        4.59      100.00
{txt}{hline 30}{c +}{hline 35}
                        Total {c |}{res}      1,025      100.00
{txt}
{com}. pwcorr MVC_scale age race_dummy1-race_dummy5, sig // correlations between MVC performance & race

             {txt}{c |} MVC_sc~e      age race_d~1 race_d~2 race_d~3 race_d~4 race_d~5
{hline 13}{c +}{hline 63}
   MVC_scale {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
         age {c |} {res}  0.0402   1.0000 
             {txt}{c |}{res}   0.2728
             {txt}{c |}
 race_dummy1 {c |} {res}  0.1726   0.1805   1.0000 
             {txt}{c |}{res}   0.0000   0.0000
             {txt}{c |}
 race_dummy2 {c |} {res} -0.1543  -0.0358  -0.4896   1.0000 
             {txt}{c |}{res}   0.0000   0.2519   0.0000
             {txt}{c |}
 race_dummy3 {c |} {res} -0.1308  -0.1422  -0.6038  -0.1423   1.0000 
             {txt}{c |}{res}   0.0003   0.0000   0.0000   0.0000
             {txt}{c |}
 race_dummy4 {c |} {res}  0.0618  -0.0979  -0.2371  -0.0559  -0.0689   1.0000 
             {txt}{c |}{res}   0.0917   0.0017   0.0000   0.0738   0.0274
             {txt}{c |}
 race_dummy5 {c |} {res}  0.0218  -0.0348  -0.3160  -0.0745  -0.0918  -0.0361   1.0000 
             {txt}{c |}{res}   0.5518   0.2655   0.0000   0.0171   0.0033   0.2488
             {txt}{c |}

{com}. 
. 
. *Comparing Models with and without controlled interactions with significant predictors
. *Original Model Without Controlled Interactions
. reg NORC_DV i.NORC_Treatment##c.MVC_scale  // model without controlled interactions

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       744
{txt}{hline 13}{c +}{hline 34}   F(3, 740)       = {res}     9.43
{txt}       Model {c |} {res}  3.7488668         3  1.24962227   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 98.0705761       740  .132527806   {txt}R-squared       ={res}    0.0368
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0329
{txt}       Total {c |} {res} 101.819443       743  .137038281   {txt}Root MSE        =   {res} .36404

{txt}{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                   NORC_DV{col 28}{c |} Coefficient{col 40}  Std. err.{col 52}      t{col 60}   P>|t|{col 68}     [95% con{col 81}f. interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}NORC_Treatment {c |}
{space 16}Treatment  {c |}{col 28}{res}{space 2}-.0379878{col 40}{space 2} .0538977{col 51}{space 1}   -0.70{col 60}{space 3}0.481{col 68}{space 4}-.1437985{col 81}{space 3} .0678229
{txt}{space 17}MVC_scale {c |}{col 28}{res}{space 2}-.0028106{col 40}{space 2} .0205879{col 51}{space 1}   -0.14{col 60}{space 3}0.891{col 68}{space 4}-.0432283{col 81}{space 3} .0376071
{txt}{space 26} {c |}
NORC_Treatment#c.MVC_scale {c |}
{space 16}Treatment  {c |}{col 28}{res}{space 2}-.0503021{col 40}{space 2} .0287265{col 51}{space 1}   -1.75{col 60}{space 3}0.080{col 68}{space 4}-.1066973{col 81}{space 3} .0060931
{txt}{space 26} {c |}
{space 21}_cons {c |}{col 28}{res}{space 2} .6091121{col 40}{space 2} .0376503{col 51}{space 1}   16.18{col 60}{space 3}0.000{col 68}{space 4} .5351979{col 81}{space 3} .6830262
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store mod1 // save estimates
{txt}
{com}. 
. *Model Without Controlled Interactions (Using significant predictors of MVC performance)
. reg NORC_DV i.NORC_Treatment##c.MVC_scale  i.NORC_Treatment##i.race_5cat ///
> i.NORC_Treatment##c.income_01 i.NORC_Treatment##c.educ_01 // // model without controlled interactions

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       744
{txt}{hline 13}{c +}{hline 34}   F(15, 728)      = {res}     6.51
{txt}       Model {c |} {res} 12.0499429        15  .803329524   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res}    89.7695       728  .123309753   {txt}R-squared       ={res}    0.1183
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1002
{txt}       Total {c |} {res} 101.819443       743  .137038281   {txt}Root MSE        =   {res} .35115

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                    NORC_DV{col 29}{c |} Coefficient{col 41}  Std. err.{col 53}      t{col 61}   P>|t|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}NORC_Treatment {c |}
{space 17}Treatment  {c |}{col 29}{res}{space 2}-.1219291{col 41}{space 2} .1246144{col 52}{space 1}   -0.98{col 61}{space 3}0.328{col 69}{space 4}-.3665757{col 82}{space 3} .1227175
{txt}{space 18}MVC_scale {c |}{col 29}{res}{space 2} .0340889{col 41}{space 2} .0209073{col 52}{space 1}    1.63{col 61}{space 3}0.103{col 69}{space 4}-.0069568{col 82}{space 3} .0751347
{txt}{space 27} {c |}
{space 1}NORC_Treatment#c.MVC_scale {c |}
{space 17}Treatment  {c |}{col 29}{res}{space 2}-.0727093{col 41}{space 2} .0291678{col 52}{space 1}   -2.49{col 61}{space 3}0.013{col 69}{space 4}-.1299724{col 82}{space 3}-.0154462
{txt}{space 27} {c |}
{space 18}race_5cat {c |}
Non-Hispanic African-Ame..  {c |}{col 29}{res}{space 2}  .236285{col 41}{space 2} .0609053{col 52}{space 1}    3.88{col 61}{space 3}0.000{col 69}{space 4}  .116714{col 82}{space 3} .3558559
{txt}{space 18}Hispanic  {c |}{col 29}{res}{space 2} .1735501{col 41}{space 2} .0519723{col 52}{space 1}    3.34{col 61}{space 3}0.001{col 69}{space 4} .0715165{col 82}{space 3} .2755836
{txt}{space 21}Asian  {c |}{col 29}{res}{space 2}-.0361674{col 41}{space 2} .1280771{col 52}{space 1}   -0.28{col 61}{space 3}0.778{col 69}{space 4} -.287612{col 82}{space 3} .2152771
{txt}{space 21}Other  {c |}{col 29}{res}{space 2} .1264234{col 41}{space 2} .0883241{col 52}{space 1}    1.43{col 61}{space 3}0.153{col 69}{space 4} -.046977{col 82}{space 3} .2998237
{txt}{space 27} {c |}
{space 3}NORC_Treatment#race_5cat {c |}
{space 17}Treatment #{c |}
Non-Hispanic African-Ame..  {c |}{col 29}{res}{space 2}-.0087023{col 41}{space 2} .0869009{col 52}{space 1}   -0.10{col 61}{space 3}0.920{col 69}{space 4}-.1793087{col 82}{space 3} .1619041
{txt}{space 8}Treatment#Hispanic  {c |}{col 29}{res}{space 2}-.0808466{col 41}{space 2}  .075723{col 52}{space 1}   -1.07{col 61}{space 3}0.286{col 69}{space 4}-.2295081{col 82}{space 3} .0678149
{txt}{space 11}Treatment#Asian  {c |}{col 29}{res}{space 2} .1392147{col 41}{space 2} .1716669{col 52}{space 1}    0.81{col 61}{space 3}0.418{col 69}{space 4}-.1978065{col 82}{space 3} .4762359
{txt}{space 11}Treatment#Other  {c |}{col 29}{res}{space 2} .1282455{col 41}{space 2} .1335109{col 52}{space 1}    0.96{col 61}{space 3}0.337{col 69}{space 4}-.1338667{col 82}{space 3} .3903578
{txt}{space 27} {c |}
{space 18}income_01 {c |}{col 29}{res}{space 2}-.2673964{col 41}{space 2} .0838101{col 52}{space 1}   -3.19{col 61}{space 3}0.001{col 69}{space 4}-.4319347{col 82}{space 3}-.1028581
{txt}{space 27} {c |}
{space 1}NORC_Treatment#c.income_01 {c |}
{space 17}Treatment  {c |}{col 29}{res}{space 2} .1298778{col 41}{space 2} .1165192{col 52}{space 1}    1.11{col 61}{space 3}0.265{col 69}{space 4} -.098876{col 82}{space 3} .3586316
{txt}{space 27} {c |}
{space 20}educ_01 {c |}{col 29}{res}{space 2}-.0387846{col 41}{space 2}  .133757{col 52}{space 1}   -0.29{col 61}{space 3}0.772{col 69}{space 4}-.3013801{col 82}{space 3} .2238108
{txt}{space 27} {c |}
{space 3}NORC_Treatment#c.educ_01 {c |}
{space 17}Treatment  {c |}{col 29}{res}{space 2} .0992009{col 41}{space 2} .1887231{col 52}{space 1}    0.53{col 61}{space 3}0.599{col 69}{space 4}-.2713056{col 82}{space 3} .4697074
{txt}{space 27} {c |}
{space 22}_cons {c |}{col 29}{res}{space 2} .6524935{col 41}{space 2} .0849488{col 52}{space 1}    7.68{col 61}{space 3}0.000{col 69}{space 4} .4857197{col 82}{space 3} .8192673
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store mod2 // save estimates
{txt}
{com}. 
. *Create estimates table for comparisons
. estimates table mod1 mod2, /// 
> b(%10.3f) se(%4.2f) stats(N r2 r2_a rmse) // little change in CATE size (now slightly stronger)
{res}
{txt}{hline 12}{c -}{c TT}{c -}{hline 10}{c -}{c -}{c -}{hline 10}{c -}{c -}
{ralign 12:Variable} {c |} {center 10:mod1} {space 1} {center 10:mod2} {space 1}
{hline 12}{c -}{c +}{c -}{hline 10}{c -}{c -}{c -}{hline 10}{c -}{c -}
{res}{txt}NORC_Treat~t {c |}
{space 2}Treatment  {c |}{res} {ralign 10:-0.038}{txt} {space 1}{res} {ralign 10:-0.122}{txt} {space 1}
{res}{txt}{space 12} {c |}{res} {ralign 10:0.05}{txt} {space 1}{res} {ralign 10:0.12}{txt} {space 1}
{res}{txt}{space 12} {c |}
{space 3}MVC_scale {c |}{res} {ralign 10:-0.003}{txt} {space 1}{res} {ralign 10:0.034}{txt} {space 1}
{res}{txt}{space 12} {c |}{res} {ralign 10:0.02}{txt} {space 1}{res} {ralign 10:0.02}{txt} {space 1}
{res}{txt}{space 12} {c |}
NORC_Treat~t#{c |}
{space 1}c.MVC_scale {c |}
{space 2}Treatment  {c |}{res} {ralign 10:-0.050}{txt} {space 1}{res} {ralign 10:-0.073}{txt} {space 1}
{res}{txt}{space 12} {c |}{res} {ralign 10:0.03}{txt} {space 1}{res} {ralign 10:0.03}{txt} {space 1}
{res}{txt}{space 12} {c |}
{space 3}race_5cat {c |}
Non-Hispa..  {c |}{res} {ralign 10:}{txt} {space 1}{res} {ralign 10:0.236}{txt} {space 1}
{res}{txt}{space 12} {c |}{res} {ralign 10:}{txt} {space 1}{res} {ralign 10:0.06}{txt} {space 1}
{res}{txt}{space 3}Hispanic  {c |}{res} {ralign 10:}{txt} {space 1}{res} {ralign 10:0.174}{txt} {space 1}
{res}{txt}{space 12} {c |}{res} {ralign 10:}{txt} {space 1}{res} {ralign 10:0.05}{txt} {space 1}
{res}{txt}{space 6}Asian  {c |}{res} {ralign 10:}{txt} {space 1}{res} {ralign 10:-0.036}{txt} {space 1}
{res}{txt}{space 12} {c |}{res} {ralign 10:}{txt} {space 1}{res} {ralign 10:0.13}{txt} {space 1}
{res}{txt}{space 6}Other  {c |}{res} {ralign 10:}{txt} {space 1}{res} {ralign 10:0.126}{txt} {space 1}
{res}{txt}{space 12} {c |}{res} {ralign 10:}{txt} {space 1}{res} {ralign 10:0.09}{txt} {space 1}
{res}{txt}{space 12} {c |}
NORC_Treat~t#{c |}
{space 3}race_5cat {c |}
{space 2}Treatment #{c |}
Non-Hispa..  {c |}{res} {ralign 10:}{txt} {space 1}{res} {ralign 10:-0.009}{txt} {space 1}
{res}{txt}{space 12} {c |}{res} {ralign 10:}{txt} {space 1}{res} {ralign 10:0.09}{txt} {space 1}
{res}{txt}{space 2}Treatment #{c |}
{space 3}Hispanic  {c |}{res} {ralign 10:}{txt} {space 1}{res} {ralign 10:-0.081}{txt} {space 1}
{res}{txt}{space 12} {c |}{res} {ralign 10:}{txt} {space 1}{res} {ralign 10:0.08}{txt} {space 1}
{res}{txt}{space 2}Treatment #{c |}
{space 6}Asian  {c |}{res} {ralign 10:}{txt} {space 1}{res} {ralign 10:0.139}{txt} {space 1}
{res}{txt}{space 12} {c |}{res} {ralign 10:}{txt} {space 1}{res} {ralign 10:0.17}{txt} {space 1}
{res}{txt}{space 2}Treatment #{c |}
{space 6}Other  {c |}{res} {ralign 10:}{txt} {space 1}{res} {ralign 10:0.128}{txt} {space 1}
{res}{txt}{space 12} {c |}{res} {ralign 10:}{txt} {space 1}{res} {ralign 10:0.13}{txt} {space 1}
{res}{txt}{space 12} {c |}
{space 3}income_01 {c |}{res} {ralign 10:}{txt} {space 1}{res} {ralign 10:-0.267}{txt} {space 1}
{res}{txt}{space 12} {c |}{res} {ralign 10:}{txt} {space 1}{res} {ralign 10:0.08}{txt} {space 1}
{res}{txt}{space 12} {c |}
NORC_Treat~t#{c |}
{space 1}c.income_01 {c |}
{space 2}Treatment  {c |}{res} {ralign 10:}{txt} {space 1}{res} {ralign 10:0.130}{txt} {space 1}
{res}{txt}{space 12} {c |}{res} {ralign 10:}{txt} {space 1}{res} {ralign 10:0.12}{txt} {space 1}
{res}{txt}{space 12} {c |}
{space 5}educ_01 {c |}{res} {ralign 10:}{txt} {space 1}{res} {ralign 10:-0.039}{txt} {space 1}
{res}{txt}{space 12} {c |}{res} {ralign 10:}{txt} {space 1}{res} {ralign 10:0.13}{txt} {space 1}
{res}{txt}{space 12} {c |}
NORC_Treat~t#{c |}
{space 3}c.educ_01 {c |}
{space 2}Treatment  {c |}{res} {ralign 10:}{txt} {space 1}{res} {ralign 10:0.099}{txt} {space 1}
{res}{txt}{space 12} {c |}{res} {ralign 10:}{txt} {space 1}{res} {ralign 10:0.19}{txt} {space 1}
{res}{txt}{space 12} {c |}
{space 7}_cons {c |}{res} {ralign 10:0.609}{txt} {space 1}{res} {ralign 10:0.652}{txt} {space 1}
{res}{txt}{space 12} {c |}{res} {ralign 10:0.04}{txt} {space 1}{res} {ralign 10:0.08}{txt} {space 1}
{res}{txt}{hline 12}{c -}{c +}{c -}{hline 10}{c -}{c -}{c -}{hline 10}{c -}{c -}
{ralign 12:N} {c |}{res} {ralign 10:744}{txt} {space 1}{res} {ralign 10:744}{txt} {space 1}
{res}{txt}{ralign 12:r2} {c |}{res} {ralign 10:0.037}{txt} {space 1}{res} {ralign 10:0.118}{txt} {space 1}
{res}{txt}{ralign 12:r2_a} {c |}{res} {ralign 10:0.033}{txt} {space 1}{res} {ralign 10:0.100}{txt} {space 1}
{res}{txt}{ralign 12:rmse} {c |}{res} {ralign 10:0.364}{txt} {space 1}{res} {ralign 10:0.351}{txt} {space 1}
{res}{txt}{hline 12}{c -}{c BT}{c -}{hline 10}{c -}{c -}{c -}{hline 10}{c -}{c -}
{ralign 40:Legend: b/se}
{res}{txt}
{com}. 
. 
. 
. ***************************
. *Table E1 Analyses (NORC)
. ***************************
. 
. *Better MVC Performance Predicts Greater Time Spent
. reg MVdisplaytime_logged MVC_scale_01 // Predicting time spent on Mock Vignette (164% increase)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       746
{txt}{hline 13}{c +}{hline 34}   F(1, 744)       = {res}   128.43
{txt}       Model {c |} {res} 193.268631         1  193.268631   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 1119.59218       744  1.50482819   {txt}R-squared       ={res}    0.1472
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1461
{txt}       Total {c |} {res} 1312.86081       745  1.76222927   {txt}Root MSE        =   {res} 1.2267

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}MVdisplayt~d{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
MVC_scale_01 {c |}{col 14}{res}{space 2} 1.639524{col 26}{space 2} .1446707{col 37}{space 1}   11.33{col 46}{space 3}0.000{col 54}{space 4} 1.355513{col 67}{space 3} 1.923536
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.077423{col 26}{space 2} .0907005{col 37}{space 1}   22.90{col 46}{space 3}0.000{col 54}{space 4} 1.899364{col 67}{space 3} 2.255483
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg EXPdisplaytime_control_logged MVC_scale_01 // Predicting time spent on control vignette (68% increase)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       363
{txt}{hline 13}{c +}{hline 34}   F(1, 361)       = {res}    17.77
{txt}       Model {c |} {res} 16.0345728         1  16.0345728   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 325.740519       361  .902328307   {txt}R-squared       ={res}    0.0469
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0443
{txt}       Total {c |} {res} 341.775091       362  .944130087   {txt}Root MSE        =   {res} .94991

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}EXP~l_logged{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
MVC_scale_01 {c |}{col 14}{res}{space 2} .6793744{col 26}{space 2}  .161162{col 37}{space 1}    4.22{col 46}{space 3}0.000{col 54}{space 4}   .36244{col 67}{space 3} .9963087
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.762414{col 26}{space 2} .0982421{col 37}{space 1}   17.94{col 46}{space 3}0.000{col 54}{space 4} 1.569216{col 67}{space 3} 1.955613
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg EXPdisplaytime_logged MVC_scale_01 // Predicting time spent on treatment condition (122% increase)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       383
{txt}{hline 13}{c +}{hline 34}   F(1, 381)       = {res}    40.96
{txt}       Model {c |} {res} 55.0873222         1  55.0873222   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 512.373344       381  1.34481193   {txt}R-squared       ={res}    0.0971
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0947
{txt}       Total {c |} {res} 567.460666       382  1.48549913   {txt}Root MSE        =   {res} 1.1597

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}EXPdisplay..{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
MVC_scale_01 {c |}{col 14}{res}{space 2} 1.221971{col 26}{space 2} .1909264{col 37}{space 1}    6.40{col 46}{space 3}0.000{col 54}{space 4} .8465699{col 67}{space 3} 1.597373
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.313252{col 26}{space 2} .1227588{col 37}{space 1}   18.84{col 46}{space 3}0.000{col 54}{space 4} 2.071882{col 67}{space 3} 2.554621
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg EXPOutcomedisplaytime_logged MVC_scale_01 // Predicting time spent on experiment outcome measure (ns)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       746
{txt}{hline 13}{c +}{hline 34}   F(1, 744)       = {res}     1.75
{txt}       Model {c |} {res} 1.33603869         1  1.33603869   {txt}Prob > F        ={res}    0.1866
{txt}    Residual {c |} {res} 568.839129       744  .764568721   {txt}R-squared       ={res}    0.0023
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0010
{txt}       Total {c |} {res} 570.175167       745  .765335795   {txt}Root MSE        =   {res}  .8744

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}EXPOutcome~d{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
MVC_scale_01 {c |}{col 14}{res}{space 2} -.136316{col 26}{space 2} .1031206{col 37}{space 1}   -1.32{col 46}{space 3}0.187{col 54}{space 4}-.3387579{col 67}{space 3}  .066126
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.050496{col 26}{space 2} .0646509{col 37}{space 1}   31.72{col 46}{space 3}0.000{col 54}{space 4} 1.923576{col 67}{space 3} 2.177416
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg duration_SB_logged MVC_scale_01  // Predicting time spent on survey (88% increase)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       746
{txt}{hline 13}{c +}{hline 34}   F(1, 744)       = {res}    31.42
{txt}       Model {c |} {res} 55.5835664         1  55.5835664   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 1316.06831       744  1.76890903   {txt}R-squared       ={res}    0.0405
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0392
{txt}       Total {c |} {res} 1371.65188       745  1.84114346   {txt}Root MSE        =   {res}   1.33

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}duration_S~d{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
MVC_scale_01 {c |}{col 14}{res}{space 2}  .879246{col 26}{space 2} .1568519{col 37}{space 1}    5.61{col 46}{space 3}0.000{col 54}{space 4} .5713208{col 67}{space 3} 1.187171
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .6000115{col 26}{space 2} .0983375{col 37}{space 1}    6.10{col 46}{space 3}0.000{col 54}{space 4} .4069596{col 67}{space 3} .7930634
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Better MVC Performance Predicts Higher Pr(Answering Experiment FMC Correctly) 
. logit FMC_correct MVC_scale_01 // Predicting pr(passing the FMC)

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-515.41094}  
Iteration 1:{space 3}log likelihood = {res:-496.88954}  
Iteration 2:{space 3}log likelihood = {res:-496.86712}  
Iteration 3:{space 3}log likelihood = {res:-496.86712}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:746}
{txt}{col 57}{lalign 13:LR chi2({res:1})}{col 70} = {res}{ralign 6:37.09}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-496.86712}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0360}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} FMC_correct{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
MVC_scale_01 {c |}{col 14}{res}{space 2}  1.47752{col 26}{space 2} .2491885{col 37}{space 1}    5.93{col 46}{space 3}0.000{col 54}{space 4} .9891194{col 67}{space 3}  1.96592
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.6649075{col 26}{space 2} .1537503{col 37}{space 1}   -4.32{col 46}{space 3}0.000{col 54}{space 4}-.9662525{col 67}{space 3}-.3635624
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, dydx(MVC_scale_01) // effect of MVC performance = .35
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:746}
{txt}{col 1}Model VCE: {res:OIM}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Pr(FMC_correct), predict()}{p_end}
{p2col:dy/dx wrt:}{res:MVC_scale_01}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
MVC_scale_01 {c |}{col 14}{res}{space 2} .3497323{col 26}{space 2} .0533548{col 37}{space 1}    6.55{col 46}{space 3}0.000{col 54}{space 4} .2451588{col 67}{space 3} .4543058
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. ********************
. *Appendix G
. ********************
. *Proportion of sample randomly assigned to not receive an MV
. proportion No_MV_Shown
{res}
{txt}{col 1}Proportion estimation{col 43}{lalign 13:Number of obs}{col 56} = {res}{ralign 5:1,025}

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 15}{c |}{col 38}             L{col 52}ogit
{col 15}{c |} Proportion{col 27}   Std. err.{col 39}     [95% con{col 52}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 2}No_MV_Shown {c |}
{space 4}Shown MV  {c |}{col 15}{res}{space 2} .7278049{col 27}{space 2} .0139023{col 38}{space 5}  .699687{col 52}{space 3} .7542149
{txt}Not Shown MV  {c |}{col 15}{res}{space 2} .2721951{col 27}{space 2} .0139023{col 38}{space 5} .2457851{col 52}{space 3}  .300313
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. 
. 
. 
. 
. ********************
. ********************
. *MTURK Study 2 Data
. ********************
. ********************
. 
. use "MTURK2_replicationdata.dta", clear
{txt}
{com}. 
. reg Opposition_01 i.Treatment  // Experiment ITT replication

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       804
{txt}{hline 13}{c +}{hline 34}   F(1, 802)       = {res}   424.59
{txt}       Model {c |} {res} 34.1284766         1  34.1284766   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 64.4650815       802  .080380401   {txt}R-squared       ={res}    0.3462
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.3453
{txt}       Total {c |} {res} 98.5935581       803  .122781517   {txt}Root MSE        =   {res} .28351

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}Oppositio~01{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}Treatment {c |}
{space 7}Lazy  {c |}{col 14}{res}{space 2} .4120612{col 26}{space 2} .0199976{col 37}{space 1}   20.61{col 46}{space 3}0.000{col 54}{space 4} .3728074{col 67}{space 3} .4513151
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .3029925{col 26}{space 2}  .014158{col 37}{space 1}   21.40{col 46}{space 3}0.000{col 54}{space 4} .2752013{col 67}{space 3} .3307837
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *FIGURE 2
. reg Opposition_01 i.Treatment##c.MVC_scale  // Interaction model for CATE estimates

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       804
{txt}{hline 13}{c +}{hline 34}   F(3, 800)       = {res}   187.43
{txt}       Model {c |} {res} 40.6947383         3  13.5649128   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 57.8988198       800  .072373525   {txt}R-squared       ={res}    0.4128
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.4106
{txt}       Total {c |} {res} 98.5935581       803  .122781517   {txt}Root MSE        =   {res} .26902

{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        Opposition_01{col 23}{c |} Coefficient{col 35}  Std. err.{col 47}      t{col 55}   P>|t|{col 63}     [95% con{col 76}f. interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}Treatment {c |}
{space 16}Lazy  {c |}{col 23}{res}{space 2} .0803773{col 35}{space 2} .0419364{col 46}{space 1}    1.92{col 55}{space 3}0.056{col 63}{space 4}-.0019411{col 76}{space 3} .1626957
{txt}{space 12}MVC_scale {c |}{col 23}{res}{space 2}-.1168992{col 35}{space 2}  .013408{col 46}{space 1}   -8.72{col 55}{space 3}0.000{col 63}{space 4}-.1432182{col 76}{space 3}-.0905801
{txt}{space 21} {c |}
Treatment#c.MVC_scale {c |}
{space 16}Lazy  {c |}{col 23}{res}{space 2} .1681181{col 35}{space 2} .0189228{col 46}{space 1}    8.88{col 55}{space 3}0.000{col 63}{space 4} .1309738{col 76}{space 3} .2052624
{txt}{space 21} {c |}
{space 16}_cons {c |}{col 23}{res}{space 2} .5330011{col 35}{space 2}  .029605{col 46}{space 1}   18.00{col 55}{space 3}0.000{col 63}{space 4} .4748884{col 76}{space 3} .5911138
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, dydx(Treatment) at(MVC_scale=(0(1)3)) // generate estimates for figure
{res}
{txt}{col 1}Conditional marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:804}
{txt}{col 1}Model VCE: {res:OLS}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.Treatment}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 9:MVC_scale} = {res:{ralign 1:0}}
{lalign 7:2._at: }{space 0}{lalign 9:MVC_scale} = {res:{ralign 1:1}}
{lalign 7:3._at: }{space 0}{lalign 9:MVC_scale} = {res:{ralign 1:2}}
{lalign 7:4._at: }{space 0}{lalign 9:MVC_scale} = {res:{ralign 1:3}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.Treatment {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.Treatment  {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .0803773{col 26}{space 2} .0419364{col 37}{space 1}    1.92{col 46}{space 3}0.056{col 54}{space 4}-.0019411{col 67}{space 3} .1626957
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .2484954{col 26}{space 2} .0264841{col 37}{space 1}    9.38{col 46}{space 3}0.000{col 54}{space 4} .1965089{col 67}{space 3} .3004819
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .4166136{col 26}{space 2} .0189815{col 37}{space 1}   21.95{col 46}{space 3}0.000{col 54}{space 4} .3793541{col 67}{space 3}  .453873
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .5847317{col 26}{space 2} .0271171{col 37}{space 1}   21.56{col 46}{space 3}0.000{col 54}{space 4} .5315026{col 67}{space 3} .6379608
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. 
. *Code for Figure 2
. marginsplot, scheme(538bw) legend(ring(0) pos(1))  ///
> xlabel(,grid glpattern(solid) glcolor(gs14)) ///
> ylabel(,grid glpattern(solid) glcolor(gs14)) ///
> xmtick(##2, ticks grid glpattern(solid) glcolor(gs14))  ///
> ymtick(##2, ticks grid glpattern(solid) glcolor(gs14)) ///
> title("") ytitle("{c -(}bf: Effect of Treatment on Opposition to Social Welfare{c )-}") ///
> xtitle("Number of Correct Mock Vignette Checks (MVCs)") ///
> plotopts(lcolor(black) lwidth(medium) mcolor(black) msymbol(circle) msize(medlarge)) ///
> ciopts(lcolor(black)) recastci(rspike) ///
> addplot(hist MVC_scale, discrete ///
> gap(30) ///
> title( " ") ///
> ytitle("Conditional Effect of 'Lazy' Treatment" ///
> "on Opposition to Social Welfare", size(small)) ///
> legend(off) ///
> xtitle("Number of Correct Mock Vignette Checks (MVCs)", size(small)) ///
> yaxis(2) yscale(alt axis(2)) percent ///
> ylabel(0 "0%" 5 "5%" 10 "10%" 15 "15%" 20 "20%" 25 "25%" 30 "30%" 35 "35%" 40 "40%", labcolor(black*.9) axis(2)) ///
> ylab(0(.2).6) ///
> ytitle("Percent of Sample", axis(2) orientation(rvertical))  ///
> yscale(titlegap(2) outergap(1)) ///
> fcolor(gs12%40) fintensity(100) lcolor(none)) ///
> xsize(6.5) ysize(3.8) graphregion(margin(vsmall))
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:MVC_scale}{p_end}
{res}{txt}
{com}. 
. graph export "Fig2b.pdf", as(pdf) replace // create Figure 2b (bottom panel)
{txt}{p 0 4 2}
file {bf}
/Users/JohnVKane/Desktop/Analyze Attentive R&R/Replication Files/PSRM STATA Replication Files/Fig2b.pdf{rm}
saved as
PDF
format
{p_end}

{com}. 
. 
. *Performance on the MVC scale
. tab MVC_scale // % passing n number of MVCs

  {txt}MVC_scale {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}         95       11.82       11.82
{txt}          1 {c |}{res}        129       16.04       27.86
{txt}          2 {c |}{res}        280       34.83       62.69
{txt}          3 {c |}{res}        300       37.31      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        804      100.00
{txt}
{com}. 
. *Pairwise correlations
. pwcorr MVC1 MVC2 MVC3, sig // correlations between MVCs

             {txt}{c |}     MVC1     MVC2     MVC3
{hline 13}{c +}{hline 27}
        MVC1 {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
        MVC2 {c |} {res}  0.2569   1.0000 
             {txt}{c |}{res}   0.0000
             {txt}{c |}
        MVC3 {c |} {res}  0.4982   0.2903   1.0000 
             {txt}{c |}{res}   0.0000   0.0000
             {txt}{c |}

{com}. 
. *Cronbach's alpha values
. alpha MVC1 MVC2 MVC3, item // alpha statistic for MVC scale

{txt}Test scale = mean(unstandardized items)

                                                            Average
                             Item-test     Item-rest       interitem
Item         {c |}  Obs  Sign   correlation   correlation     covariance      alpha
{hline 13}{c +}{hline 65}
MVC1{col 14}{c |}{res}{col 16} 804{col 24}+{col 31} 0.7437{col 45} 0.4612{col 59} .0639765{col 73} 0.4477
{txt}MVC2{col 14}{c |}{res}{col 16} 804{col 24}+{col 31} 0.7253{col 45} 0.3170{col 59} .0878608{col 73} 0.6624
{txt}MVC3{col 14}{c |}{res}{col 16} 804{col 24}+{col 31} 0.7832{col 45} 0.4810{col 59} .0507642{col 73} 0.4009
{txt}{hline 13}{c +}{hline 65}
Test scale{col 14}{c |}{res}{col 59} .0675339{col 73} 0.6036
{txt}{hline 13}{c BT}{hline 65}

{com}. 
. ********************
. ** Table A2 Analyses
. ********************
. ci means q10_pagesubmit // average time on screen

{txt}    Variable {c |}        Obs        Mean    Std. err.       [95% conf. interval]
{hline 13}{c +}{hline 63}
q10_pagesu~t {c |}{col 16}{res}       804{col 29} 37.69936{col 41} 2.178371{col 57} 33.42338{col 69} 41.97533{txt}

{com}. proportion MVC1 MVC2 MVC3 // proportion passing each MVC
{res}
{txt}{col 1}Proportion estimation{col 44}{lalign 13:Number of obs}{col 57} = {res}{ralign 3:804}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 37}             L{col 51}ogit
{col 14}{c |} Proportion{col 26}   Std. err.{col 38}     [95% con{col 51}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 8}MVC1 {c |}
{space 2}Incorrect  {c |}{col 14}{res}{space 2} .1965174{col 26}{space 2}  .014014{col 37}{space 5} .1704531{col 51}{space 3}  .225484
{txt}{space 4}Correct  {c |}{col 14}{res}{space 2} .8034826{col 26}{space 2}  .014014{col 37}{space 5}  .774516{col 51}{space 3} .8295469
{txt}{space 12} {c |}
{space 8}MVC2 {c |}
{space 2}Incorrect  {c |}{col 14}{res}{space 2} .5584577{col 26}{space 2} .0175127{col 37}{space 5} .5238557{col 51}{space 3} .5925012
{txt}{space 4}Correct  {c |}{col 14}{res}{space 2} .4415423{col 26}{space 2} .0175127{col 37}{space 5} .4074988{col 51}{space 3} .4761443
{txt}{space 12} {c |}
{space 8}MVC3 {c |}
{space 2}Incorrect  {c |}{col 14}{res}{space 2} .2686567{col 26}{space 2} .0156326{col 37}{space 5} .2390991{col 51}{space 3} .3004256
{txt}{space 4}Correct  {c |}{col 14}{res}{space 2} .7313433{col 26}{space 2} .0156326{col 37}{space 5} .6995744{col 51}{space 3} .7609009
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. 
. di .8034826 -.1667 // difference b/w proportion passing MVC1 vs. chance
{res}.6367826
{txt}
{com}. prtest MVC1==.1667 // significance test for this difference

{txt}One-sample test of proportion                   Number of obs      = {res}      804

{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. err.{col 44}{col 49}{col 59}[95% conf. interval]
{hline 13}{c +}{hline 64}
{col 9}MVC1{col 14}{c |}{res}{col 17} .8034826{col 28}  .014014{col 58} .7760157{col 70} .8309494
{txt}{hline 13}{c BT}{hline 64}
    p = proportion({res}MVC1{txt})                                          z = {res} 48.4452
{txt}H0: p = {res}0.1667

   {txt}Ha: p < {res}0.1667               {txt}Ha: p != {res}0.1667               {txt}Ha: p > {res}0.1667
 {txt}Pr(Z < z) = {res}1.0000         {txt}Pr(|Z| > |z|) = {res}0.0000          {txt}Pr(Z > z) = {res}0.0000
{txt}
{com}. 
. di .4415423-.1667 // difference b/w proportion passing MVC2 vs. chance
{res}.2748423
{txt}
{com}. prtest MVC2==.2 // significance test for this difference

{txt}One-sample test of proportion                   Number of obs      = {res}      804

{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. err.{col 44}{col 49}{col 59}[95% conf. interval]
{hline 13}{c +}{hline 64}
{col 9}MVC2{col 14}{c |}{res}{col 17} .4415423{col 28} .0175127{col 58}  .407218{col 70} .4758666
{txt}{hline 13}{c BT}{hline 64}
    p = proportion({res}MVC2{txt})                                          z = {res} 17.1223
{txt}H0: p = {res}0.2

     {txt}Ha: p < {res}0.2                 {txt}Ha: p != {res}0.2                   {txt}Ha: p > {res}0.2
 {txt}Pr(Z < z) = {res}1.0000         {txt}Pr(|Z| > |z|) = {res}0.0000          {txt}Pr(Z > z) = {res}0.0000
{txt}
{com}. 
. di .7313433-.1667 // difference b/w proportion passing MVC2 vs. chance
{res}.5646433
{txt}
{com}. prtest MVC3==.1667 // significance test for this difference

{txt}One-sample test of proportion                   Number of obs      = {res}      804

{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. err.{col 44}{col 49}{col 59}[95% conf. interval]
{hline 13}{c +}{hline 64}
{col 9}MVC3{col 14}{c |}{res}{col 17} .7313433{col 28} .0156326{col 58} .7007039{col 70} .7619826
{txt}{hline 13}{c BT}{hline 64}
    p = proportion({res}MVC3{txt})                                          z = {res} 42.9570
{txt}H0: p = {res}0.1667

   {txt}Ha: p < {res}0.1667               {txt}Ha: p != {res}0.1667               {txt}Ha: p > {res}0.1667
 {txt}Pr(Z < z) = {res}1.0000         {txt}Pr(|Z| > |z|) = {res}0.0000          {txt}Pr(Z > z) = {res}0.0000
{txt}
{com}. 
. 
. ********************
. *Table A7 Analyses
. ********************
. bysort Treatment:  tab MVC_scale // % passing n number of MVCs by experimental condition

{txt}{hline}
-> Treatment = Unlucky

  MVC_scale {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}         48       11.97       11.97
{txt}          1 {c |}{res}         64       15.96       27.93
{txt}          2 {c |}{res}        142       35.41       63.34
{txt}          3 {c |}{res}        147       36.66      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        401      100.00

{txt}{hline}
-> Treatment = Lazy

  MVC_scale {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}         47       11.66       11.66
{txt}          1 {c |}{res}         65       16.13       27.79
{txt}          2 {c |}{res}        138       34.24       62.03
{txt}          3 {c |}{res}        153       37.97      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        403      100.00

{txt}
{com}. tab MVC_scale // overall % passing n number of MVCs

  {txt}MVC_scale {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}         95       11.82       11.82
{txt}          1 {c |}{res}        129       16.04       27.86
{txt}          2 {c |}{res}        280       34.83       62.69
{txt}          3 {c |}{res}        300       37.31      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        804      100.00
{txt}
{com}. 
. 
. ********************
. *Table B1. Demographic Results (restricted to those featured in model)
. ********************
. reg Opposition_01 i.Treatment##c.MVC_scale  // Model for calculating demographic stats

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       804
{txt}{hline 13}{c +}{hline 34}   F(3, 800)       = {res}   187.43
{txt}       Model {c |} {res} 40.6947383         3  13.5649128   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 57.8988198       800  .072373525   {txt}R-squared       ={res}    0.4128
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.4106
{txt}       Total {c |} {res} 98.5935581       803  .122781517   {txt}Root MSE        =   {res} .26902

{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        Opposition_01{col 23}{c |} Coefficient{col 35}  Std. err.{col 47}      t{col 55}   P>|t|{col 63}     [95% con{col 76}f. interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}Treatment {c |}
{space 16}Lazy  {c |}{col 23}{res}{space 2} .0803773{col 35}{space 2} .0419364{col 46}{space 1}    1.92{col 55}{space 3}0.056{col 63}{space 4}-.0019411{col 76}{space 3} .1626957
{txt}{space 12}MVC_scale {c |}{col 23}{res}{space 2}-.1168992{col 35}{space 2}  .013408{col 46}{space 1}   -8.72{col 55}{space 3}0.000{col 63}{space 4}-.1432182{col 76}{space 3}-.0905801
{txt}{space 21} {c |}
Treatment#c.MVC_scale {c |}
{space 16}Lazy  {c |}{col 23}{res}{space 2} .1681181{col 35}{space 2} .0189228{col 46}{space 1}    8.88{col 55}{space 3}0.000{col 63}{space 4} .1309738{col 76}{space 3} .2052624
{txt}{space 21} {c |}
{space 16}_cons {c |}{col 23}{res}{space 2} .5330011{col 35}{space 2}  .029605{col 46}{space 1}   18.00{col 55}{space 3}0.000{col 63}{space 4} .4748884{col 76}{space 3} .5911138
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
.         *// Descriptive stats for income age, education and political interest
. tabstat income age polint educ if e(sample), st(mean p50) 

{txt}   Stats {...}
{c |}{...}
    income       age    polint      educ
{hline 9}{c +}{hline 40}
{ralign 8:Mean} {...}
{c |}{...}
 {res}        3  38.27861  3.441542  3.746269
{txt}{ralign 8:p50} {...}
{c |}{...}
 {res}        3        35         3         4
{txt}{hline 9}{c BT}{hline 40}

{com}. 
. tab gender if e(sample) // % female

     {txt}gender {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       Male {c |}{res}        397       49.38       49.38
{txt}     Female {c |}{res}        407       50.62      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        804      100.00
{txt}
{com}. tab race_5cat if e(sample) // % of each racial group

               {txt}RECODE of race {c |}      Freq.     Percent        Cum.
{hline 30}{c +}{hline 35}
           Non-Hispanic White {c |}{res}        598       74.38       74.38
{txt}Non-Hispanic African-American {c |}{res}         69        8.58       82.96
{txt}                     Hispanic {c |}{res}         67        8.33       91.29
{txt}                        Asian {c |}{res}         49        6.09       97.39
{txt}                        Other {c |}{res}         21        2.61      100.00
{txt}{hline 30}{c +}{hline 35}
                        Total {c |}{res}        804      100.00
{txt}
{com}. tab pid7 if e(sample) // % of each partisan group

             {txt}pid7 {c |}      Freq.     Percent        Cum.
{hline 18}{c +}{hline 35}
  Strong Democrat {c |}{res}        111       13.81       13.81
{txt}         Democrat {c |}{res}        205       25.50       39.30
{txt}    Lean Democrat {c |}{res}        109       13.56       52.86
{txt}      Independent {c |}{res}        137       17.04       69.90
{txt}  Lean Republican {c |}{res}         70        8.71       78.61
{txt}       Republican {c |}{res}        133       16.54       95.15
{txt}Strong Republican {c |}{res}         39        4.85      100.00
{txt}{hline 18}{c +}{hline 35}
            Total {c |}{res}        804      100.00
{txt}
{com}. tab ideology if e(sample) // % of each ideological group

              {txt}ideology {c |}      Freq.     Percent        Cum.
{hline 23}{c +}{hline 35}
     Extremely Liberal {c |}{res}         79        9.83        9.83
{txt}               Liberal {c |}{res}        193       24.00       33.83
{txt}      Slightly Liberal {c |}{res}        112       13.93       47.76
{txt}              Moderate {c |}{res}        170       21.14       68.91
{txt} Slightly Conservative {c |}{res}        103       12.81       81.72
{txt}          Conservative {c |}{res}        104       12.94       94.65
{txt}Extremely Conservative {c |}{res}         43        5.35      100.00
{txt}{hline 23}{c +}{hline 35}
                 Total {c |}{res}        804      100.00
{txt}
{com}.  
. 
. *Additional information
. tab income if e(sample) // // frequency distribution for income

     {txt}income {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
      0-25k {c |}{res}        112       13.93       13.93
{txt}    25k-50k {c |}{res}        228       28.36       42.29
{txt}    50k-75k {c |}{res}        193       24.00       66.29
{txt}    75-100k {c |}{res}        140       17.41       83.71
{txt}  100k-150k {c |}{res}         96       11.94       95.65
{txt}   150-200k {c |}{res}         20        2.49       98.13
{txt}  Over 200k {c |}{res}         15        1.87      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        804      100.00
{txt}
{com}. tab educ if e(sample) // frequency distribution for education

                 {txt}educ {c |}      Freq.     Percent        Cum.
{hline 22}{c +}{hline 35}
                  <HS {c |}{res}          4        0.50        0.50
{txt}              HS grad {c |}{res}         66        8.21        8.71
{txt}         Some college {c |}{res}        195       24.25       32.96
{txt}         College grad {c |}{res}        416       51.74       84.70
{txt}              Masters {c |}{res}        111       13.81       98.51
{txt}PhD or other advanced {c |}{res}         12        1.49      100.00
{txt}{hline 22}{c +}{hline 35}
                Total {c |}{res}        804      100.00
{txt}
{com}. tab polint if e(sample) // frequency distribution for political interest

              {txt}polint {c |}      Freq.     Percent        Cum.
{hline 21}{c +}{hline 35}
      Not interested {c |}{res}         28        3.48        3.48
{txt}                   2 {c |}{res}        105       13.06       16.54
{txt}                   3 {c |}{res}        288       35.82       52.36
{txt}                   4 {c |}{res}        250       31.09       83.46
{txt}Extremely interested {c |}{res}        133       16.54      100.00
{txt}{hline 21}{c +}{hline 35}
               Total {c |}{res}        804      100.00
{txt}
{com}. 
. ********************
. *Table D1 Analyses
. ********************
. *Demographic predictors of MVC performance
. reg MVC_scale_01 i.gender i.race_5cat age_01 income_01 educ_01 polint_01 pid7_01 ideology_01 

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       804
{txt}{hline 13}{c +}{hline 34}   F(11, 792)      = {res}     9.32
{txt}       Model {c |} {res} 10.2935504        11   .93577731   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 79.5454483       792  .100436172   {txt}R-squared       ={res}    0.1146
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1023
{txt}       Total {c |} {res} 89.8389987       803  .111879201   {txt}Root MSE        =   {res} .31692

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}               MVC_scale_01{col 29}{c |} Coefficient{col 41}  Std. err.{col 53}      t{col 61}   P>|t|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}gender {c |}
{space 20}Female  {c |}{col 29}{res}{space 2} .0722771{col 41}{space 2} .0228178{col 52}{space 1}    3.17{col 61}{space 3}0.002{col 69}{space 4} .0274865{col 82}{space 3} .1170676
{txt}{space 27} {c |}
{space 18}race_5cat {c |}
Non-Hispanic African-Ame..  {c |}{col 29}{res}{space 2}-.0992619{col 41}{space 2}  .040821{col 52}{space 1}   -2.43{col 61}{space 3}0.015{col 69}{space 4}-.1793921{col 82}{space 3}-.0191317
{txt}{space 18}Hispanic  {c |}{col 29}{res}{space 2}-.1618831{col 41}{space 2} .0415255{col 52}{space 1}   -3.90{col 61}{space 3}0.000{col 69}{space 4}-.2433963{col 82}{space 3}  -.08037
{txt}{space 21}Asian  {c |}{col 29}{res}{space 2}-.0303045{col 41}{space 2} .0481824{col 52}{space 1}   -0.63{col 61}{space 3}0.530{col 69}{space 4}-.1248849{col 82}{space 3} .0642758
{txt}{space 21}Other  {c |}{col 29}{res}{space 2}-.2441421{col 41}{space 2} .0708998{col 52}{space 1}   -3.44{col 61}{space 3}0.001{col 69}{space 4}-.3833159{col 82}{space 3}-.1049684
{txt}{space 27} {c |}
{space 21}age_01 {c |}{col 29}{res}{space 2}  .324777{col 41}{space 2} .0568641{col 52}{space 1}    5.71{col 61}{space 3}0.000{col 69}{space 4} .2131549{col 82}{space 3} .4363991
{txt}{space 18}income_01 {c |}{col 29}{res}{space 2} .0711905{col 41}{space 2} .0506659{col 52}{space 1}    1.41{col 61}{space 3}0.160{col 69}{space 4}-.0282648{col 82}{space 3} .1706457
{txt}{space 20}educ_01 {c |}{col 29}{res}{space 2}-.1191523{col 41}{space 2} .0703595{col 52}{space 1}   -1.69{col 61}{space 3}0.091{col 69}{space 4}-.2572655{col 82}{space 3} .0189609
{txt}{space 18}polint_01 {c |}{col 29}{res}{space 2}-.0618402{col 41}{space 2}  .046011{col 52}{space 1}   -1.34{col 61}{space 3}0.179{col 69}{space 4}-.1521581{col 82}{space 3} .0284777
{txt}{space 20}pid7_01 {c |}{col 29}{res}{space 2}-.0151596{col 41}{space 2} .0520183{col 52}{space 1}   -0.29{col 61}{space 3}0.771{col 69}{space 4}-.1172697{col 82}{space 3} .0869505
{txt}{space 16}ideology_01 {c |}{col 29}{res}{space 2}-.0812744{col 41}{space 2} .0545511{col 52}{space 1}   -1.49{col 61}{space 3}0.137{col 69}{space 4}-.1883561{col 82}{space 3} .0258074
{txt}{space 22}_cons {c |}{col 29}{res}{space 2} .6622606{col 41}{space 2} .0549099{col 52}{space 1}   12.06{col 61}{space 3}0.000{col 69}{space 4} .5544744{col 82}{space 3} .7700469
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Generate table D1
. outreg2 using TableD1.doc, append ctitle(MTurk 2) dec(2) e(r2_a) ///
> alpha(0.001, 0.01, 0.05, .10) symbol(***, **, *,^) 
{txt}{stata `"shellout using `"TableD1.doc"'"':TableD1.doc}
{browse `"/Users/JohnVKane/Desktop/Analyze Attentive R&R/Replication Files/PSRM STATA Replication Files"' :dir}{com} : {txt}{stata `"seeout using "TableD1.txt""':seeout}

{com}. 
. * Other information Reported in Appendix D 
. *Correlations between race, age, and MVC performance
. tab race_5cat, gen(race_dummy) // generate racial dummy variables

               {txt}RECODE of race {c |}      Freq.     Percent        Cum.
{hline 30}{c +}{hline 35}
           Non-Hispanic White {c |}{res}        598       74.38       74.38
{txt}Non-Hispanic African-American {c |}{res}         69        8.58       82.96
{txt}                     Hispanic {c |}{res}         67        8.33       91.29
{txt}                        Asian {c |}{res}         49        6.09       97.39
{txt}                        Other {c |}{res}         21        2.61      100.00
{txt}{hline 30}{c +}{hline 35}
                        Total {c |}{res}        804      100.00
{txt}
{com}. pwcorr MVC_scale age race_dummy1-race_dummy5, sig // correlations between race and MVC performance

             {txt}{c |} MVC_sc~e      age race_d~1 race_d~2 race_d~3 race_d~4 race_d~5
{hline 13}{c +}{hline 63}
   MVC_scale {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
         age {c |} {res}  0.2356   1.0000 
             {txt}{c |}{res}   0.0000
             {txt}{c |}
 race_dummy1 {c |} {res}  0.1907   0.2032   1.0000 
             {txt}{c |}{res}   0.0000   0.0000
             {txt}{c |}
 race_dummy2 {c |} {res} -0.0592  -0.0327  -0.5220   1.0000 
             {txt}{c |}{res}   0.0935   0.3545   0.0000
             {txt}{c |}
 race_dummy3 {c |} {res} -0.1544  -0.1577  -0.5137  -0.0924   1.0000 
             {txt}{c |}{res}   0.0000   0.0000   0.0000   0.0088
             {txt}{c |}
 race_dummy4 {c |} {res} -0.0147  -0.0921  -0.4341  -0.0781  -0.0768   1.0000 
             {txt}{c |}{res}   0.6766   0.0090   0.0000   0.0269   0.0294
             {txt}{c |}
 race_dummy5 {c |} {res} -0.1283  -0.0874  -0.2790  -0.0502  -0.0494  -0.0417   1.0000 
             {txt}{c |}{res}   0.0003   0.0131   0.0000   0.1552   0.1619   0.2373
             {txt}{c |}

{com}. 
. *Comparing Models with and without controlled interactions with significant predictors
. *Original Model Without Controlled Interactions
. reg Opposition_01 i.Treatment##c.MVC_scale  // model without controlled interactions

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       804
{txt}{hline 13}{c +}{hline 34}   F(3, 800)       = {res}   187.43
{txt}       Model {c |} {res} 40.6947383         3  13.5649128   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 57.8988198       800  .072373525   {txt}R-squared       ={res}    0.4128
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.4106
{txt}       Total {c |} {res} 98.5935581       803  .122781517   {txt}Root MSE        =   {res} .26902

{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        Opposition_01{col 23}{c |} Coefficient{col 35}  Std. err.{col 47}      t{col 55}   P>|t|{col 63}     [95% con{col 76}f. interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}Treatment {c |}
{space 16}Lazy  {c |}{col 23}{res}{space 2} .0803773{col 35}{space 2} .0419364{col 46}{space 1}    1.92{col 55}{space 3}0.056{col 63}{space 4}-.0019411{col 76}{space 3} .1626957
{txt}{space 12}MVC_scale {c |}{col 23}{res}{space 2}-.1168992{col 35}{space 2}  .013408{col 46}{space 1}   -8.72{col 55}{space 3}0.000{col 63}{space 4}-.1432182{col 76}{space 3}-.0905801
{txt}{space 21} {c |}
Treatment#c.MVC_scale {c |}
{space 16}Lazy  {c |}{col 23}{res}{space 2} .1681181{col 35}{space 2} .0189228{col 46}{space 1}    8.88{col 55}{space 3}0.000{col 63}{space 4} .1309738{col 76}{space 3} .2052624
{txt}{space 21} {c |}
{space 16}_cons {c |}{col 23}{res}{space 2} .5330011{col 35}{space 2}  .029605{col 46}{space 1}   18.00{col 55}{space 3}0.000{col 63}{space 4} .4748884{col 76}{space 3} .5911138
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store mod1 // store estmates
{txt}
{com}. 
. *Model Without Controlled Interactions (Using significant predictors of MVC performance)
.         // model without controlled interactions
. reg Opposition_01 i.Treatment##c.MVC_scale  i.Treatment##i.race_5cat i.Treatment##c.age_01

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       804
{txt}{hline 13}{c +}{hline 34}   F(13, 790)      = {res}    48.84
{txt}       Model {c |} {res} 43.9303598        13  3.37925844   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 54.6631984       790  .069193922   {txt}R-squared       ={res}    0.4456
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.4364
{txt}       Total {c |} {res} 98.5935581       803  .122781517   {txt}Root MSE        =   {res} .26305

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}              Opposition_01{col 29}{c |} Coefficient{col 41}  Std. err.{col 53}      t{col 61}   P>|t|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 18}Treatment {c |}
{space 22}Lazy  {c |}{col 29}{res}{space 2}-.0206568{col 41}{space 2} .0525026{col 52}{space 1}   -0.39{col 61}{space 3}0.694{col 69}{space 4} -.123718{col 82}{space 3} .0824044
{txt}{space 18}MVC_scale {c |}{col 29}{res}{space 2} -.098605{col 41}{space 2} .0137393{col 52}{space 1}   -7.18{col 61}{space 3}0.000{col 69}{space 4}-.1255748{col 82}{space 3}-.0716352
{txt}{space 27} {c |}
{space 6}Treatment#c.MVC_scale {c |}
{space 22}Lazy  {c |}{col 29}{res}{space 2} .1385738{col 41}{space 2} .0194171{col 52}{space 1}    7.14{col 61}{space 3}0.000{col 69}{space 4} .1004587{col 82}{space 3}  .176689
{txt}{space 27} {c |}
{space 18}race_5cat {c |}
Non-Hispanic African-Ame..  {c |}{col 29}{res}{space 2} .0024461{col 41}{space 2} .0513825{col 52}{space 1}    0.05{col 61}{space 3}0.962{col 69}{space 4}-.0984164{col 82}{space 3} .1033085
{txt}{space 18}Hispanic  {c |}{col 29}{res}{space 2} .1643887{col 41}{space 2} .0484952{col 52}{space 1}    3.39{col 61}{space 3}0.001{col 69}{space 4} .0691941{col 82}{space 3} .2595834
{txt}{space 21}Asian  {c |}{col 29}{res}{space 2}  .038005{col 41}{space 2} .0596519{col 52}{space 1}    0.64{col 61}{space 3}0.524{col 69}{space 4}  -.07909{col 82}{space 3}    .1551
{txt}{space 21}Other  {c |}{col 29}{res}{space 2}  .087605{col 41}{space 2} .0820954{col 52}{space 1}    1.07{col 61}{space 3}0.286{col 69}{space 4} -.073546{col 82}{space 3}  .248756
{txt}{space 27} {c |}
{space 8}Treatment#race_5cat {c |}
{space 22}Lazy #{c |}
Non-Hispanic African-Ame..  {c |}{col 29}{res}{space 2}-.0351034{col 41}{space 2}  .068039{col 52}{space 1}   -0.52{col 61}{space 3}0.606{col 69}{space 4} -.168662{col 82}{space 3} .0984551
{txt}{space 13}Lazy#Hispanic  {c |}{col 29}{res}{space 2}-.1198107{col 41}{space 2} .0696072{col 52}{space 1}   -1.72{col 61}{space 3}0.086{col 69}{space 4}-.2564477{col 82}{space 3} .0168262
{txt}{space 16}Lazy#Asian  {c |}{col 29}{res}{space 2} .0307033{col 41}{space 2} .0794689{col 52}{space 1}    0.39{col 61}{space 3}0.699{col 69}{space 4} -.125292{col 82}{space 3} .1866985
{txt}{space 16}Lazy#Other  {c |}{col 29}{res}{space 2}-.0501353{col 41}{space 2} .1186603{col 52}{space 1}   -0.42{col 61}{space 3}0.673{col 69}{space 4} -.283062{col 82}{space 3} .1827913
{txt}{space 27} {c |}
{space 21}age_01 {c |}{col 29}{res}{space 2}-.2333048{col 41}{space 2} .0680083{col 52}{space 1}   -3.43{col 61}{space 3}0.001{col 69}{space 4}-.3668032{col 82}{space 3}-.0998065
{txt}{space 27} {c |}
{space 9}Treatment#c.age_01 {c |}
{space 22}Lazy  {c |}{col 29}{res}{space 2} .5000423{col 41}{space 2} .0947472{col 52}{space 1}    5.28{col 61}{space 3}0.000{col 69}{space 4} .3140562{col 82}{space 3} .6860284
{txt}{space 27} {c |}
{space 22}_cons {c |}{col 29}{res}{space 2}  .559477{col 41}{space 2} .0372027{col 52}{space 1}   15.04{col 61}{space 3}0.000{col 69}{space 4} .4864491{col 82}{space 3} .6325049
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estimates store mod2 // store estimates
{txt}
{com}. 
. *Generate estimates table
. estimates table mod1 mod2, /// table showing estimates from above models
> b(%10.3f) se(%4.2f) stats(N r2 r2_a rmse) // little change in CATE size
{res}
{txt}{hline 12}{c -}{c TT}{c -}{hline 10}{c -}{c -}{c -}{hline 10}{c -}{c -}
{ralign 12:Variable} {c |} {center 10:mod1} {space 1} {center 10:mod2} {space 1}
{hline 12}{c -}{c +}{c -}{hline 10}{c -}{c -}{c -}{hline 10}{c -}{c -}
{res}{txt}{space 3}Treatment {c |}
{space 7}Lazy  {c |}{res} {ralign 10:0.080}{txt} {space 1}{res} {ralign 10:-0.021}{txt} {space 1}
{res}{txt}{space 12} {c |}{res} {ralign 10:0.04}{txt} {space 1}{res} {ralign 10:0.05}{txt} {space 1}
{res}{txt}{space 12} {c |}
{space 3}MVC_scale {c |}{res} {ralign 10:-0.117}{txt} {space 1}{res} {ralign 10:-0.099}{txt} {space 1}
{res}{txt}{space 12} {c |}{res} {ralign 10:0.01}{txt} {space 1}{res} {ralign 10:0.01}{txt} {space 1}
{res}{txt}{space 12} {c |}
{space 3}Treatment#{c |}
{space 1}c.MVC_scale {c |}
{space 7}Lazy  {c |}{res} {ralign 10:0.168}{txt} {space 1}{res} {ralign 10:0.139}{txt} {space 1}
{res}{txt}{space 12} {c |}{res} {ralign 10:0.02}{txt} {space 1}{res} {ralign 10:0.02}{txt} {space 1}
{res}{txt}{space 12} {c |}
{space 3}race_5cat {c |}
Non-Hispa..  {c |}{res} {ralign 10:}{txt} {space 1}{res} {ralign 10:0.002}{txt} {space 1}
{res}{txt}{space 12} {c |}{res} {ralign 10:}{txt} {space 1}{res} {ralign 10:0.05}{txt} {space 1}
{res}{txt}{space 3}Hispanic  {c |}{res} {ralign 10:}{txt} {space 1}{res} {ralign 10:0.164}{txt} {space 1}
{res}{txt}{space 12} {c |}{res} {ralign 10:}{txt} {space 1}{res} {ralign 10:0.05}{txt} {space 1}
{res}{txt}{space 6}Asian  {c |}{res} {ralign 10:}{txt} {space 1}{res} {ralign 10:0.038}{txt} {space 1}
{res}{txt}{space 12} {c |}{res} {ralign 10:}{txt} {space 1}{res} {ralign 10:0.06}{txt} {space 1}
{res}{txt}{space 6}Other  {c |}{res} {ralign 10:}{txt} {space 1}{res} {ralign 10:0.088}{txt} {space 1}
{res}{txt}{space 12} {c |}{res} {ralign 10:}{txt} {space 1}{res} {ralign 10:0.08}{txt} {space 1}
{res}{txt}{space 12} {c |}
{space 3}Treatment#{c |}
{space 3}race_5cat {c |}
{space 7}Lazy #{c |}
Non-Hispa..  {c |}{res} {ralign 10:}{txt} {space 1}{res} {ralign 10:-0.035}{txt} {space 1}
{res}{txt}{space 12} {c |}{res} {ralign 10:}{txt} {space 1}{res} {ralign 10:0.07}{txt} {space 1}
{res}{txt}{space 7}Lazy #{c |}
{space 3}Hispanic  {c |}{res} {ralign 10:}{txt} {space 1}{res} {ralign 10:-0.120}{txt} {space 1}
{res}{txt}{space 12} {c |}{res} {ralign 10:}{txt} {space 1}{res} {ralign 10:0.07}{txt} {space 1}
{res}{txt}{space 1}Lazy#Asian  {c |}{res} {ralign 10:}{txt} {space 1}{res} {ralign 10:0.031}{txt} {space 1}
{res}{txt}{space 12} {c |}{res} {ralign 10:}{txt} {space 1}{res} {ralign 10:0.08}{txt} {space 1}
{res}{txt}{space 1}Lazy#Other  {c |}{res} {ralign 10:}{txt} {space 1}{res} {ralign 10:-0.050}{txt} {space 1}
{res}{txt}{space 12} {c |}{res} {ralign 10:}{txt} {space 1}{res} {ralign 10:0.12}{txt} {space 1}
{res}{txt}{space 12} {c |}
{space 6}age_01 {c |}{res} {ralign 10:}{txt} {space 1}{res} {ralign 10:-0.233}{txt} {space 1}
{res}{txt}{space 12} {c |}{res} {ralign 10:}{txt} {space 1}{res} {ralign 10:0.07}{txt} {space 1}
{res}{txt}{space 12} {c |}
{space 3}Treatment#{c |}
{space 4}c.age_01 {c |}
{space 7}Lazy  {c |}{res} {ralign 10:}{txt} {space 1}{res} {ralign 10:0.500}{txt} {space 1}
{res}{txt}{space 12} {c |}{res} {ralign 10:}{txt} {space 1}{res} {ralign 10:0.09}{txt} {space 1}
{res}{txt}{space 12} {c |}
{space 7}_cons {c |}{res} {ralign 10:0.533}{txt} {space 1}{res} {ralign 10:0.559}{txt} {space 1}
{res}{txt}{space 12} {c |}{res} {ralign 10:0.03}{txt} {space 1}{res} {ralign 10:0.04}{txt} {space 1}
{res}{txt}{hline 12}{c -}{c +}{c -}{hline 10}{c -}{c -}{c -}{hline 10}{c -}{c -}
{ralign 12:N} {c |}{res} {ralign 10:804}{txt} {space 1}{res} {ralign 10:804}{txt} {space 1}
{res}{txt}{ralign 12:r2} {c |}{res} {ralign 10:0.413}{txt} {space 1}{res} {ralign 10:0.446}{txt} {space 1}
{res}{txt}{ralign 12:r2_a} {c |}{res} {ralign 10:0.411}{txt} {space 1}{res} {ralign 10:0.436}{txt} {space 1}
{res}{txt}{ralign 12:rmse} {c |}{res} {ralign 10:0.269}{txt} {space 1}{res} {ralign 10:0.263}{txt} {space 1}
{res}{txt}{hline 12}{c -}{c BT}{c -}{hline 10}{c -}{c -}{c -}{hline 10}{c -}{c -}
{ralign 40:Legend: b/se}
{res}{txt}
{com}. 
. 
. ********************
. *Table E1 Analyses
. ********************
. 
. *Better MVC Performance Predicts Greater Time Spent
. reg q10_pagesubmit_logged MVC_scale_01 // Predicting time on MV (196% increase)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       804
{txt}{hline 13}{c +}{hline 34}   F(1, 802)       = {res}   411.39
{txt}       Model {c |} {res} 346.610812         1  346.610812   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 675.718347       802   .84254158   {txt}R-squared       ={res}    0.3390
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.3382
{txt}       Total {c |} {res} 1022.32916       803  1.27313718   {txt}Root MSE        =   {res}  .9179

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}q10_pagesu~d{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
MVC_scale_01 {c |}{col 14}{res}{space 2} 1.964213{col 26}{space 2} .0968419{col 37}{space 1}   20.28{col 46}{space 3}0.000{col 54}{space 4}  1.77412{col 67}{space 3} 2.154307
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  1.81091{col 26}{space 2} .0715414{col 37}{space 1}   25.31{col 46}{space 3}0.000{col 54}{space 4}  1.67048{col 67}{space 3} 1.951341
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg q54_pagesubmit_logged MVC_scale_01 // Predicting time on control vignette (unlucky) (141% increase)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       401
{txt}{hline 13}{c +}{hline 34}   F(1, 399)       = {res}   111.31
{txt}       Model {c |} {res} 88.7133284         1  88.7133284   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 317.988369       399  .796963332   {txt}R-squared       ={res}    0.2181
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.2162
{txt}       Total {c |} {res} 406.701698       400  1.01675424   {txt}Root MSE        =   {res} .89273

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}q54_pagesu~d{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
MVC_scale_01 {c |}{col 14}{res}{space 2} 1.408284{col 26}{space 2} .1334797{col 37}{space 1}   10.55{col 46}{space 3}0.000{col 54}{space 4} 1.145873{col 67}{space 3} 1.670696
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.757285{col 26}{space 2} .0982415{col 37}{space 1}   17.89{col 46}{space 3}0.000{col 54}{space 4} 1.564149{col 67}{space 3} 1.950421
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg q12_pagesubmit_logged MVC_scale_01 // Predicting time on treatment vignette (lazy) (204% increase)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       403
{txt}{hline 13}{c +}{hline 34}   F(1, 401)       = {res}   220.78
{txt}       Model {c |} {res} 187.398529         1  187.398529   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 340.374675       401   .84881465   {txt}R-squared       ={res}    0.3551
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.3535
{txt}       Total {c |} {res} 527.773204       402  1.31286867   {txt}Root MSE        =   {res} .92131

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}q12_pagesu~d{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
MVC_scale_01 {c |}{col 14}{res}{space 2} 2.038399{col 26}{space 2} .1371868{col 37}{space 1}   14.86{col 46}{space 3}0.000{col 54}{space 4} 1.768703{col 67}{space 3} 2.308094
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.215713{col 26}{space 2} .1017188{col 37}{space 1}   11.95{col 46}{space 3}0.000{col 54}{space 4} 1.015745{col 67}{space 3} 1.415682
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg q11_pagesubmit_logged MVC_scale_01 // Predicting time on experiment outcome (79% increase)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       804
{txt}{hline 13}{c +}{hline 34}   F(1, 802)       = {res}   131.18
{txt}       Model {c |} {res} 55.7318531         1  55.7318531   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 340.739395       802  .424862088   {txt}R-squared       ={res}    0.1406
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1395
{txt}       Total {c |} {res} 396.471248       803  .493737544   {txt}Root MSE        =   {res} .65181

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}q11_pagesu~d{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
MVC_scale_01 {c |}{col 14}{res}{space 2} .7876246{col 26}{space 2} .0687688{col 37}{space 1}   11.45{col 46}{space 3}0.000{col 54}{space 4} .6526365{col 67}{space 3} .9226127
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.662611{col 26}{space 2} .0508026{col 37}{space 1}   32.73{col 46}{space 3}0.000{col 54}{space 4} 1.562889{col 67}{space 3} 1.762333
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg duration_logged MVC_scale_01 // Predicting time on survey in total (57% increase)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       804
{txt}{hline 13}{c +}{hline 34}   F(1, 802)       = {res}   103.82
{txt}       Model {c |} {res} 29.5553611         1  29.5553611   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 228.312261       802   .28467863   {txt}R-squared       ={res}    0.1146
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1135
{txt}       Total {c |} {res} 257.867622       803  .321130289   {txt}Root MSE        =   {res} .53355

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}duration_l~d{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
MVC_scale_01 {c |}{col 14}{res}{space 2}  .573569{col 26}{space 2} .0562918{col 37}{space 1}   10.19{col 46}{space 3}0.000{col 54}{space 4} .4630724{col 67}{space 3} .6840656
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 5.660967{col 26}{space 2} .0415852{col 37}{space 1}  136.13{col 46}{space 3}0.000{col 54}{space 4} 5.579339{col 67}{space 3} 5.742596
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Better MVC Performance Predicts Higher Pr(Answering Experiment FMC Correctly) 
. logit FMC_correct MVC_scale_01 // Predicting pr(passing the FMC)

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-392.71508}  
Iteration 1:{space 3}log likelihood = {res:-304.08989}  
Iteration 2:{space 3}log likelihood = {res:-294.23555}  
Iteration 3:{space 3}log likelihood = {res:-294.12813}  
Iteration 4:{space 3}log likelihood = {res:-294.12811}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:804}
{txt}{col 57}{lalign 13:LR chi2({res:1})}{col 70} = {res}{ralign 6:197.17}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-294.12811}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.2510}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} FMC_correct{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
MVC_scale_01 {c |}{col 14}{res}{space 2} 4.017441{col 26}{space 2} .3303074{col 37}{space 1}   12.16{col 46}{space 3}0.000{col 54}{space 4} 3.370051{col 67}{space 3} 4.664832
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.7159859{col 26}{space 2} .1800606{col 37}{space 1}   -3.98{col 46}{space 3}0.000{col 54}{space 4}-1.068898{col 67}{space 3}-.3630737
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, dydx(MVC_scale_01) // effect of performance on the MVC scale = .45
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:804}
{txt}{col 1}Model VCE: {res:OIM}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Pr(FMC_correct), predict()}{p_end}
{p2col:dy/dx wrt:}{res:MVC_scale_01}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
MVC_scale_01 {c |}{col 14}{res}{space 2} .4536477{col 26}{space 2} .0242543{col 37}{space 1}   18.70{col 46}{space 3}0.000{col 54}{space 4} .4061102{col 67}{space 3} .5011853
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. 
. **************
. **************
. *MTURK 1 Study
. **************
. **************
. 
. use "MTURK1_replicationdata.dta", clear
{txt}
{com}. 
. reg SLexpDV i.SL_Treatment // Experiment ITT replication

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       603
{txt}{hline 13}{c +}{hline 34}   F(1, 601)       = {res}    14.29
{txt}       Model {c |} {res} 59.7335661         1  59.7335661   {txt}Prob > F        ={res}    0.0002
{txt}    Residual {c |} {res} 2511.43061       601   4.1787531   {txt}R-squared       ={res}    0.0232
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0216
{txt}       Total {c |} {res} 2571.16418       602  4.27103684   {txt}Root MSE        =   {res} 2.0442

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     SLexpDV{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
SL_Treatment {c |}
{space 2}Treatment  {c |}{col 14}{res}{space 2}-.6294996{col 26}{space 2} .1664982{col 37}{space 1}   -3.78{col 46}{space 3}0.000{col 54}{space 4}-.9564887{col 67}{space 3}-.3025106
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 5.073579{col 26}{space 2} .1182191{col 37}{space 1}   42.92{col 46}{space 3}0.000{col 54}{space 4} 4.841406{col 67}{space 3} 5.305751
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg SLexpDV i.SL_Treatment if MVC_Correct==0 // effect among MVC non-passers = -.41 (ns)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       173
{txt}{hline 13}{c +}{hline 34}   F(1, 171)       = {res}     2.10
{txt}       Model {c |} {res}  7.1651765         1   7.1651765   {txt}Prob > F        ={res}    0.1495
{txt}    Residual {c |} {res}  584.60361       171  3.41873456   {txt}R-squared       ={res}    0.0121
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0063
{txt}       Total {c |} {res} 591.768786       172   3.4405162   {txt}Root MSE        =   {res}  1.849

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     SLexpDV{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
SL_Treatment {c |}
{space 2}Treatment  {c |}{col 14}{res}{space 2}-.4070856{col 26}{space 2} .2811933{col 37}{space 1}   -1.45{col 46}{space 3}0.150{col 54}{space 4}-.9621425{col 67}{space 3} .1479714
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 4.941176{col 26}{space 2} .2005503{col 37}{space 1}   24.64{col 46}{space 3}0.000{col 54}{space 4} 4.545304{col 67}{space 3} 5.337049
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg SLexpDV i.SL_Treatment if MVC_Correct==1 // effect among MVC passers = -.72 (p<.001)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       430
{txt}{hline 13}{c +}{hline 34}   F(1, 428)       = {res}    12.36
{txt}       Model {c |} {res} 55.5351381         1  55.5351381   {txt}Prob > F        ={res}    0.0005
{txt}    Residual {c |} {res} 1923.74161       428  4.49472338   {txt}R-squared       ={res}    0.0281
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0258
{txt}       Total {c |} {res} 1979.27674       429   4.6136987   {txt}Root MSE        =   {res} 2.1201

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     SLexpDV{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
SL_Treatment {c |}
{space 2}Treatment  {c |}{col 14}{res}{space 2}-.7187608{col 26}{space 2} .2044805{col 37}{space 1}   -3.52{col 46}{space 3}0.000{col 54}{space 4}-1.120672{col 67}{space 3}-.3168498
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 5.126168{col 26}{space 2} .1449254{col 37}{space 1}   35.37{col 46}{space 3}0.000{col 54}{space 4} 4.841314{col 67}{space 3} 5.411022
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. ********************
. ** Table A6 Analyses
. ********************
. ci means MV_time // average time and CI reading the Mock Vignette

{txt}    Variable {c |}        Obs        Mean    Std. err.       [95% conf. interval]
{hline 13}{c +}{hline 63}
     MV_time {c |}{col 16}{res}       603{col 29} 51.57868{col 41} 2.979649{col 57} 45.72691{col 69} 57.43045{txt}

{com}. proportion MVC_Correct // proportion passing the MVC (71%)
{res}
{txt}{col 1}Proportion estimation{col 44}{lalign 13:Number of obs}{col 57} = {res}{ralign 3:603}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 37}             L{col 51}ogit
{col 14}{c |} Proportion{col 26}   Std. err.{col 38}     [95% con{col 51}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 1}MVC_Correct {c |}
{space 2}Incorrect  {c |}{col 14}{res}{space 2} .2868988{col 26}{space 2} .0184196{col 37}{space 5} .2521251{col 51}{space 3} .3243884
{txt}{space 4}Correct  {c |}{col 14}{res}{space 2} .7131012{col 26}{space 2} .0184196{col 37}{space 5} .6756116{col 51}{space 3} .7478749
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. 
. di .7131012-.2 // difference between proportion passing and pr(answering correctly by chance)
{res}.5131012
{txt}
{com}. prtest MVC_Correct==.2 // significance test for above difference

{txt}One-sample test of proportion                   Number of obs      = {res}      603

{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. err.{col 44}{col 49}{col 59}[95% conf. interval]
{hline 13}{c +}{hline 64}
{col 2}MVC_Correct{col 14}{c |}{res}{col 17} .7131012{col 28} .0184196{col 58} .6769993{col 70}  .749203
{txt}{hline 13}{c BT}{hline 64}
    p = proportion({res}MVC_Correct{txt})                                   z = {res} 31.4994
{txt}H0: p = {res}0.2

     {txt}Ha: p < {res}0.2                 {txt}Ha: p != {res}0.2                   {txt}Ha: p > {res}0.2
 {txt}Pr(Z < z) = {res}1.0000         {txt}Pr(|Z| > |z|) = {res}0.0000          {txt}Pr(Z > z) = {res}0.0000
{txt}
{com}. 
. ********************
. ** Table A7 Analyses
. ********************
. 
. bysort SL_Treatment:  tab MVC_Correct // % passing n MVCs by experimental group

{txt}{hline}
-> SL_Treatment = Control

       Mock {c |}
   Vignette {c |}
        FMC {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
  Incorrect {c |}{res}         85       28.43       28.43
{txt}    Correct {c |}{res}        214       71.57      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        299      100.00

{txt}{hline}
-> SL_Treatment = Treatment

       Mock {c |}
   Vignette {c |}
        FMC {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
  Incorrect {c |}{res}         88       28.95       28.95
{txt}    Correct {c |}{res}        216       71.05      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        304      100.00

{txt}
{com}. 
. tab MVC_Correct // % passing n MVCs in overall sample

       {txt}Mock {c |}
   Vignette {c |}
        FMC {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
  Incorrect {c |}{res}        173       28.69       28.69
{txt}    Correct {c |}{res}        430       71.31      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        603      100.00
{txt}
{com}. 
. ********************
. *Table B1. Demographic Results (restricted to those featured in model)
. ********************
. reg SLexpDV i.SL_Treatment##i.MVC_Correct // regression model to calculate demographics

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       603
{txt}{hline 13}{c +}{hline 34}   F(3, 599)       = {res}     5.00
{txt}       Model {c |} {res} 62.8189634         3  20.9396545   {txt}Prob > F        ={res}    0.0020
{txt}    Residual {c |} {res} 2508.34522       599  4.18755462   {txt}R-squared       ={res}    0.0244
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0195
{txt}       Total {c |} {res} 2571.16418       602  4.27103684   {txt}Root MSE        =   {res} 2.0464

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                 SLexpDV{col 26}{c |} Coefficient{col 38}  Std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}SL_Treatment {c |}
{space 14}Treatment  {c |}{col 26}{res}{space 2}-.4070856{col 38}{space 2} .3112092{col 49}{space 1}   -1.31{col 58}{space 3}0.191{col 66}{space 4}-1.018279{col 79}{space 3} .2041083
{txt}{space 24} {c |}
{space 13}MVC_Correct {c |}
{space 16}Correct  {c |}{col 26}{res}{space 2} .1849918{col 38}{space 2} .2623611{col 49}{space 1}    0.71{col 58}{space 3}0.481{col 66}{space 4}-.3302677{col 79}{space 3} .7002512
{txt}{space 24} {c |}
SL_Treatment#MVC_Correct {c |}
{space 6}Treatment#Correct  {c |}{col 26}{res}{space 2}-.3116753{col 38}{space 2} .3685187{col 49}{space 1}   -0.85{col 58}{space 3}0.398{col 66}{space 4}-1.035421{col 79}{space 3} .4120705
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2} 4.941176{col 38}{space 2}  .221958{col 49}{space 1}   22.26{col 58}{space 3}0.000{col 66}{space 4} 4.505266{col 79}{space 3} 5.377087
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         *Descriptive stats for income, education, age and political interest
. tabstat income educ age polint if e(sample), st(mean median)

{txt}   Stats {...}
{c |}{...}
    income      educ       age    polint
{hline 9}{c +}{hline 40}
{ralign 8:Mean} {...}
{c |}{...}
 {res}  2.93864  3.706468  36.81924  3.502488
{txt}{ralign 8:p50} {...}
{c |}{...}
 {res}        3         4        34         4
{txt}{hline 9}{c BT}{hline 40}

{com}. tab1 gender race_5cat pid7 ideology if e(sample) // % female, racial groups, party and ideology

{res}-> tabulation of gender if e(sample) 

     {txt}gender {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       Male {c |}{res}        291       48.26       48.26
{txt}     Female {c |}{res}        312       51.74      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        603      100.00

-> tabulation of race_5cat if e(sample) 

        {txt}RECODE of race (race) {c |}      Freq.     Percent        Cum.
{hline 30}{c +}{hline 35}
           Non-Hispanic White {c |}{res}        431       71.48       71.48
{txt}Non-Hispanic African-American {c |}{res}         46        7.63       79.10
{txt}                     Hispanic {c |}{res}         71       11.77       90.88
{txt}                        Asian {c |}{res}         32        5.31       96.19
{txt}                        Other {c |}{res}         23        3.81      100.00
{txt}{hline 30}{c +}{hline 35}
                        Total {c |}{res}        603      100.00

-> tabulation of pid7 if e(sample) 

       {txt}pid7 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}         85       14.10       14.10
{txt}          2 {c |}{res}        146       24.21       38.31
{txt}          3 {c |}{res}         83       13.76       52.07
{txt}          4 {c |}{res}         92       15.26       67.33
{txt}          5 {c |}{res}         58        9.62       76.95
{txt}          6 {c |}{res}        103       17.08       94.03
{txt}          7 {c |}{res}         36        5.97      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        603      100.00

-> tabulation of ideology if e(sample) 

   {txt}ideology {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}         83       13.76       13.76
{txt}          2 {c |}{res}        127       21.06       34.83
{txt}          3 {c |}{res}         91       15.09       49.92
{txt}          4 {c |}{res}         95       15.75       65.67
{txt}          5 {c |}{res}         78       12.94       78.61
{txt}          6 {c |}{res}         92       15.26       93.86
{txt}          7 {c |}{res}         37        6.14      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        603      100.00
{txt}
{com}. 
. *Additional information
. tab1 educ polint income

{res}-> tabulation of educ  

                 {txt}educ {c |}      Freq.     Percent        Cum.
{hline 22}{c +}{hline 35}
                  <HS {c |}{res}          1        0.17        0.17
{txt}              HS grad {c |}{res}         58        9.62        9.78
{txt}         Some college {c |}{res}        159       26.37       36.15
{txt}         College grad {c |}{res}        298       49.42       85.57
{txt}              Masters {c |}{res}         73       12.11       97.68
{txt}PhD or other advanced {c |}{res}         14        2.32      100.00
{txt}{hline 22}{c +}{hline 35}
                Total {c |}{res}        603      100.00

-> tabulation of polint  

     {txt}polint {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}         18        2.99        2.99
{txt}          2 {c |}{res}         78       12.94       15.92
{txt}          3 {c |}{res}        200       33.17       49.09
{txt}          4 {c |}{res}        197       32.67       81.76
{txt}          5 {c |}{res}        110       18.24      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        603      100.00

-> tabulation of income  

     {txt}income {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}         78       12.94       12.94
{txt}          2 {c |}{res}        177       29.35       42.29
{txt}          3 {c |}{res}        161       26.70       68.99
{txt}          4 {c |}{res}        104       17.25       86.24
{txt}          5 {c |}{res}         63       10.45       96.68
{txt}          6 {c |}{res}         14        2.32       99.00
{txt}          7 {c |}{res}          6        1.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        603      100.00
{txt}
{com}. tab1 gender race_5cat educ polint pid7 ideology income if e(sample), nol

{res}-> tabulation of gender if e(sample) 

     {txt}gender {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}        291       48.26       48.26
{txt}          2 {c |}{res}        312       51.74      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        603      100.00

-> tabulation of race_5cat if e(sample) 

  {txt}RECODE of {c |}
race (race) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}        431       71.48       71.48
{txt}          2 {c |}{res}         46        7.63       79.10
{txt}          3 {c |}{res}         71       11.77       90.88
{txt}          4 {c |}{res}         32        5.31       96.19
{txt}          5 {c |}{res}         23        3.81      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        603      100.00

-> tabulation of educ if e(sample) 

       {txt}educ {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}          1        0.17        0.17
{txt}          2 {c |}{res}         58        9.62        9.78
{txt}          3 {c |}{res}        159       26.37       36.15
{txt}          4 {c |}{res}        298       49.42       85.57
{txt}          5 {c |}{res}         73       12.11       97.68
{txt}          6 {c |}{res}         14        2.32      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        603      100.00

-> tabulation of polint if e(sample) 

     {txt}polint {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}         18        2.99        2.99
{txt}          2 {c |}{res}         78       12.94       15.92
{txt}          3 {c |}{res}        200       33.17       49.09
{txt}          4 {c |}{res}        197       32.67       81.76
{txt}          5 {c |}{res}        110       18.24      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        603      100.00

-> tabulation of pid7 if e(sample) 

       {txt}pid7 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}         85       14.10       14.10
{txt}          2 {c |}{res}        146       24.21       38.31
{txt}          3 {c |}{res}         83       13.76       52.07
{txt}          4 {c |}{res}         92       15.26       67.33
{txt}          5 {c |}{res}         58        9.62       76.95
{txt}          6 {c |}{res}        103       17.08       94.03
{txt}          7 {c |}{res}         36        5.97      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        603      100.00

-> tabulation of ideology if e(sample) 

   {txt}ideology {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}         83       13.76       13.76
{txt}          2 {c |}{res}        127       21.06       34.83
{txt}          3 {c |}{res}         91       15.09       49.92
{txt}          4 {c |}{res}         95       15.75       65.67
{txt}          5 {c |}{res}         78       12.94       78.61
{txt}          6 {c |}{res}         92       15.26       93.86
{txt}          7 {c |}{res}         37        6.14      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        603      100.00

-> tabulation of income if e(sample) 

     {txt}income {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}         78       12.94       12.94
{txt}          2 {c |}{res}        177       29.35       42.29
{txt}          3 {c |}{res}        161       26.70       68.99
{txt}          4 {c |}{res}        104       17.25       86.24
{txt}          5 {c |}{res}         63       10.45       96.68
{txt}          6 {c |}{res}         14        2.32       99.00
{txt}          7 {c |}{res}          6        1.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        603      100.00
{txt}
{com}. 
. 
. ********************
. *Table D1 Analyses
. ********************
. 
. *Demographic predictors of MVC performance
. reg MVC_Correct i.gender i.race_5cat age_01 income_01 educ_01 polint_01 pid7_01 ideology_01  

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       603
{txt}{hline 13}{c +}{hline 34}   F(11, 591)      = {res}     6.85
{txt}       Model {c |} {res} 13.9502639        11  1.26820581   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 109.416237       591  .185137457   {txt}R-squared       ={res}    0.1131
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0966
{txt}       Total {c |} {res} 123.366501       602  .204927742   {txt}Root MSE        =   {res} .43028

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                MVC_Correct{col 29}{c |} Coefficient{col 41}  Std. err.{col 53}      t{col 61}   P>|t|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}gender {c |}
{space 20}Female  {c |}{col 29}{res}{space 2} .0586732{col 41}{space 2} .0361013{col 52}{space 1}    1.63{col 61}{space 3}0.105{col 69}{space 4}-.0122292{col 82}{space 3} .1295756
{txt}{space 27} {c |}
{space 18}race_5cat {c |}
Non-Hispanic African-Ame..  {c |}{col 29}{res}{space 2}-.1793349{col 41}{space 2} .0677046{col 52}{space 1}   -2.65{col 61}{space 3}0.008{col 69}{space 4}-.3123058{col 82}{space 3}-.0463641
{txt}{space 18}Hispanic  {c |}{col 29}{res}{space 2}-.2649936{col 41}{space 2}  .057066{col 52}{space 1}   -4.64{col 61}{space 3}0.000{col 69}{space 4}-.3770704{col 82}{space 3}-.1529168
{txt}{space 21}Asian  {c |}{col 29}{res}{space 2}-.0943287{col 41}{space 2} .0811781{col 52}{space 1}   -1.16{col 61}{space 3}0.246{col 69}{space 4}-.2537614{col 82}{space 3}  .065104
{txt}{space 21}Other  {c |}{col 29}{res}{space 2}-.1851874{col 41}{space 2}  .092525{col 52}{space 1}   -2.00{col 61}{space 3}0.046{col 69}{space 4}-.3669053{col 82}{space 3}-.0034696
{txt}{space 27} {c |}
{space 21}age_01 {c |}{col 29}{res}{space 2} .3795816{col 41}{space 2} .1081036{col 52}{space 1}    3.51{col 61}{space 3}0.000{col 69}{space 4} .1672677{col 82}{space 3} .5918956
{txt}{space 18}income_01 {c |}{col 29}{res}{space 2} .1305028{col 41}{space 2} .0854237{col 52}{space 1}    1.53{col 61}{space 3}0.127{col 69}{space 4}-.0372682{col 82}{space 3} .2982739
{txt}{space 20}educ_01 {c |}{col 29}{res}{space 2}-.1065528{col 41}{space 2} .1078666{col 52}{space 1}   -0.99{col 61}{space 3}0.324{col 69}{space 4}-.3184014{col 82}{space 3} .1052958
{txt}{space 18}polint_01 {c |}{col 29}{res}{space 2} .0159988{col 41}{space 2}  .070677{col 52}{space 1}    0.23{col 61}{space 3}0.821{col 69}{space 4}-.1228099{col 82}{space 3} .1548074
{txt}{space 20}pid7_01 {c |}{col 29}{res}{space 2}-.1065589{col 41}{space 2} .0861265{col 52}{space 1}   -1.24{col 61}{space 3}0.216{col 69}{space 4}-.2757101{col 82}{space 3} .0625924
{txt}{space 16}ideology_01 {c |}{col 29}{res}{space 2}-.1469499{col 41}{space 2} .0882237{col 52}{space 1}   -1.67{col 61}{space 3}0.096{col 69}{space 4}  -.32022{col 82}{space 3} .0263202
{txt}{space 22}_cons {c |}{col 29}{res}{space 2} .7518466{col 41}{space 2}  .079069{col 52}{space 1}    9.51{col 61}{space 3}0.000{col 69}{space 4} .5965561{col 82}{space 3} .9071371
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         *Add to Table D1
. outreg2 using TableD1.doc, append ctitle(MTurk 1) dec(2) e(r2_a) ///
> alpha(0.001, 0.01, 0.05, .10) symbol(***, **, *,^) 
{txt}{stata `"shellout using `"TableD1.doc"'"':TableD1.doc}
{browse `"/Users/JohnVKane/Desktop/Analyze Attentive R&R/Replication Files/PSRM STATA Replication Files"' :dir}{com} : {txt}{stata `"seeout using "TableD1.txt""':seeout}

{com}. 
. *Other information Reported in Appendix D
. 
. *Correlations between race, age, and MVC performance
. tab race_5cat if e(sample), gen(race_dummy) // generate racial dummy variable

        {txt}RECODE of race (race) {c |}      Freq.     Percent        Cum.
{hline 30}{c +}{hline 35}
           Non-Hispanic White {c |}{res}        431       71.48       71.48
{txt}Non-Hispanic African-American {c |}{res}         46        7.63       79.10
{txt}                     Hispanic {c |}{res}         71       11.77       90.88
{txt}                        Asian {c |}{res}         32        5.31       96.19
{txt}                        Other {c |}{res}         23        3.81      100.00
{txt}{hline 30}{c +}{hline 35}
                        Total {c |}{res}        603      100.00
{txt}
{com}. pwcorr MVC_Correct age race_dummy1-race_dummy5 if e(sample), sig // correlations between race & MVC performance

             {txt}{c |} MVC_Co~t      age race_d~1 race_d~2 race_d~3 race_d~4 race_d~5
{hline 13}{c +}{hline 63}
 MVC_Correct {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
         age {c |} {res}  0.1748   1.0000 
             {txt}{c |}{res}   0.0000
             {txt}{c |}
 race_dummy1 {c |} {res}  0.2245   0.2039   1.0000 
             {txt}{c |}{res}   0.0000   0.0000
             {txt}{c |}
 race_dummy2 {c |} {res} -0.0663  -0.0659  -0.4549   1.0000 
             {txt}{c |}{res}   0.1037   0.1057   0.0000
             {txt}{c |}
 race_dummy3 {c |} {res} -0.2119  -0.1522  -0.5783  -0.1050   1.0000 
             {txt}{c |}{res}   0.0000   0.0002   0.0000   0.0099
             {txt}{c |}
 race_dummy4 {c |} {res} -0.0134  -0.1070  -0.3747  -0.0680  -0.0865   1.0000 
             {txt}{c |}{res}   0.7426   0.0085   0.0000   0.0951   0.0337
             {txt}{c |}
 race_dummy5 {c |} {res} -0.0651  -0.0079  -0.3152  -0.0572  -0.0727  -0.0471   1.0000 
             {txt}{c |}{res}   0.1102   0.8468   0.0000   0.1605   0.0742   0.2477
             {txt}{c |}

{com}. 
. *Original Model 
. reg SLexpDV i.SL_Treatment // Model for replication of main experiment

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       603
{txt}{hline 13}{c +}{hline 34}   F(1, 601)       = {res}    14.29
{txt}       Model {c |} {res} 59.7335661         1  59.7335661   {txt}Prob > F        ={res}    0.0002
{txt}    Residual {c |} {res} 2511.43061       601   4.1787531   {txt}R-squared       ={res}    0.0232
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0216
{txt}       Total {c |} {res} 2571.16418       602  4.27103684   {txt}Root MSE        =   {res} 2.0442

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     SLexpDV{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
SL_Treatment {c |}
{space 2}Treatment  {c |}{col 14}{res}{space 2}-.6294996{col 26}{space 2} .1664982{col 37}{space 1}   -3.78{col 46}{space 3}0.000{col 54}{space 4}-.9564887{col 67}{space 3}-.3025106
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 5.073579{col 26}{space 2} .1182191{col 37}{space 1}   42.92{col 46}{space 3}0.000{col 54}{space 4} 4.841406{col 67}{space 3} 5.305751
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. tab race_5cat if e(sample) //race demographics for sample of MVC passers

        {txt}RECODE of race (race) {c |}      Freq.     Percent        Cum.
{hline 30}{c +}{hline 35}
           Non-Hispanic White {c |}{res}        431       71.48       71.48
{txt}Non-Hispanic African-American {c |}{res}         46        7.63       79.10
{txt}                     Hispanic {c |}{res}         71       11.77       90.88
{txt}                        Asian {c |}{res}         32        5.31       96.19
{txt}                        Other {c |}{res}         23        3.81      100.00
{txt}{hline 30}{c +}{hline 35}
                        Total {c |}{res}        603      100.00
{txt}
{com}. sum age if e(sample) // age statistics for sample of MVC passers

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 9}age {c |}{res}        603    36.81924    11.91674         18         87
{txt}
{com}. 
. *Model Subsetting on Passing the MVC
. reg SLexpDV i.SL_Treatment if MVC_Correct==1 

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       430
{txt}{hline 13}{c +}{hline 34}   F(1, 428)       = {res}    12.36
{txt}       Model {c |} {res} 55.5351381         1  55.5351381   {txt}Prob > F        ={res}    0.0005
{txt}    Residual {c |} {res} 1923.74161       428  4.49472338   {txt}R-squared       ={res}    0.0281
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0258
{txt}       Total {c |} {res} 1979.27674       429   4.6136987   {txt}Root MSE        =   {res} 2.1201

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     SLexpDV{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
SL_Treatment {c |}
{space 2}Treatment  {c |}{col 14}{res}{space 2}-.7187608{col 26}{space 2} .2044805{col 37}{space 1}   -3.52{col 46}{space 3}0.000{col 54}{space 4}-1.120672{col 67}{space 3}-.3168498
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 5.126168{col 26}{space 2} .1449254{col 37}{space 1}   35.37{col 46}{space 3}0.000{col 54}{space 4} 4.841314{col 67}{space 3} 5.411022
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. tab race_5cat if e(sample) //race demographics for sample of MVC passers

        {txt}RECODE of race (race) {c |}      Freq.     Percent        Cum.
{hline 30}{c +}{hline 35}
           Non-Hispanic White {c |}{res}        335       77.91       77.91
{txt}Non-Hispanic African-American {c |}{res}         28        6.51       84.42
{txt}                     Hispanic {c |}{res}         32        7.44       91.86
{txt}                        Asian {c |}{res}         22        5.12       96.98
{txt}                        Other {c |}{res}         13        3.02      100.00
{txt}{hline 30}{c +}{hline 35}
                        Total {c |}{res}        430      100.00
{txt}
{com}. sum age if e(sample) // age statistics for sample of MVC passers

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 9}age {c |}{res}        430    38.13953     12.4894         18         87
{txt}
{com}. 
. ********************
. *Table E1 Analyses
. ********************
. 
. *Better MVC Performance Predicts Greater Time Spent
. reg logged_MV_time i.MVC_Correct // Predicting time spent on Mock Vignette (123% increase)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       603
{txt}{hline 13}{c +}{hline 34}   F(1, 601)       = {res}   164.87
{txt}       Model {c |} {res}  187.24323         1   187.24323   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 682.574868       601  1.13573189   {txt}R-squared       ={res}    0.2153
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.2140
{txt}       Total {c |} {res} 869.818098       602  1.44488056   {txt}Root MSE        =   {res} 1.0657

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}logged_MV_~e{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}MVC_Correct {c |}
{space 4}Correct  {c |}{col 14}{res}{space 2} 1.231982{col 26}{space 2} .0959487{col 37}{space 1}   12.84{col 46}{space 3}0.000{col 54}{space 4} 1.043547{col 67}{space 3} 1.420418
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.501521{col 26}{space 2} .0810242{col 37}{space 1}   30.87{col 46}{space 3}0.000{col 54}{space 4} 2.342396{col 67}{space 3} 2.660646
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg logged_Control_time i.MVC_Correct // Predicting time spent on control vignette (63% increase)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       299
{txt}{hline 13}{c +}{hline 34}   F(1, 297)       = {res}    43.14
{txt}       Model {c |} {res} 24.1167962         1  24.1167962   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 166.036331       297  .559044887   {txt}R-squared       ={res}    0.1268
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1239
{txt}       Total {c |} {res} 190.153127       298  .638097743   {txt}Root MSE        =   {res} .74769

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}logged_Con~e{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}MVC_Correct {c |}
{space 4}Correct  {c |}{col 14}{res}{space 2} .6296208{col 26}{space 2} .0958611{col 37}{space 1}    6.57{col 46}{space 3}0.000{col 54}{space 4} .4409676{col 67}{space 3} .8182739
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  1.46891{col 26}{space 2} .0810987{col 37}{space 1}   18.11{col 46}{space 3}0.000{col 54}{space 4} 1.309309{col 67}{space 3} 1.628511
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg logged_Treatment_time i.MVC_Correct // Predicting time spent on treatment condition (108% increase)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       304
{txt}{hline 13}{c +}{hline 34}   F(1, 302)       = {res}    82.96
{txt}       Model {c |} {res} 73.1942535         1  73.1942535   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res}  266.44157       302  .882256855   {txt}R-squared       ={res}    0.2155
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.2129
{txt}       Total {c |} {res} 339.635824       303  1.12091031   {txt}Root MSE        =   {res} .93929

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}logged_Tre~e{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}MVC_Correct {c |}
{space 4}Correct  {c |}{col 14}{res}{space 2}  1.08195{col 26}{space 2} .1187862{col 37}{space 1}    9.11{col 46}{space 3}0.000{col 54}{space 4} .8481962{col 67}{space 3} 1.315703
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.999964{col 26}{space 2} .1001281{col 37}{space 1}   19.97{col 46}{space 3}0.000{col 54}{space 4} 1.802927{col 67}{space 3} 2.197001
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg logged_Outcome_time i.MVC_Correct // Predicting time spent on experiment outcome measure (14% increase)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       603
{txt}{hline 13}{c +}{hline 34}   F(1, 601)       = {res}     6.97
{txt}       Model {c |} {res} 2.39435837         1  2.39435837   {txt}Prob > F        ={res}    0.0085
{txt}    Residual {c |} {res} 206.454131       601  .343517689   {txt}R-squared       ={res}    0.0115
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0098
{txt}       Total {c |} {res} 208.848489       602  .346924401   {txt}Root MSE        =   {res}  .5861

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}logged_Out~e{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}MVC_Correct {c |}
{space 4}Correct  {c |}{col 14}{res}{space 2} .1393144{col 26}{space 2} .0527686{col 37}{space 1}    2.64{col 46}{space 3}0.009{col 54}{space 4}  .035681{col 67}{space 3} .2429477
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.572899{col 26}{space 2} .0445606{col 37}{space 1}   35.30{col 46}{space 3}0.000{col 54}{space 4} 1.485385{col 67}{space 3} 1.660412
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg logged_Durationinseconds i.MVC_Correct  // Predicting time spent on survey (36% increase)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       603
{txt}{hline 13}{c +}{hline 34}   F(1, 601)       = {res}    53.74
{txt}       Model {c |} {res} 15.7910442         1  15.7910442   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 176.597732       601   .29383982   {txt}R-squared       ={res}    0.0821
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0806
{txt}       Total {c |} {res} 192.388776       602  .319582685   {txt}Root MSE        =   {res} .54207

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}logged_Dur~s{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}MVC_Correct {c |}
{space 4}Correct  {c |}{col 14}{res}{space 2} .3577724{col 26}{space 2} .0488041{col 37}{space 1}    7.33{col 46}{space 3}0.000{col 54}{space 4}  .261925{col 67}{space 3} .4536198
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  5.87144{col 26}{space 2} .0412128{col 37}{space 1}  142.47{col 46}{space 3}0.000{col 54}{space 4} 5.790502{col 67}{space 3} 5.952379
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Better MVC Performance Predicts Higher Pr(Answering Experiment FMC Correctly) 
. logit SL_FMC_Correct MVC_Correct  // Predicting pr(answering FMC correctly)

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-387.84325}  
Iteration 1:{space 3}log likelihood = {res:-348.49306}  
Iteration 2:{space 3}log likelihood = {res:-348.21964}  
Iteration 3:{space 3}log likelihood = {res:-348.21959}  
Iteration 4:{space 3}log likelihood = {res:-348.21959}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:603}
{txt}{col 57}{lalign 13:LR chi2({res:1})}{col 70} = {res}{ralign 6:79.25}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-348.21959}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.1022}

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}SL_FMC_Correct{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      z{col 48}   P>|z|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}MVC_Correct {c |}{col 16}{res}{space 2} 1.677097{col 28}{space 2} .1937204{col 39}{space 1}    8.66{col 48}{space 3}0.000{col 56}{space 4} 1.297411{col 69}{space 3} 2.056782
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}-.4831741{col 28}{space 2} .1565162{col 39}{space 1}   -3.09{col 48}{space 3}0.002{col 56}{space 4}-.7899401{col 69}{space 3} -.176408
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, dydx(MVC_Correct) // effect of MVC performance on FMC passage = .33
{res}
{txt}{col 1}Average marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:603}
{txt}{col 1}Model VCE: {res:OIM}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Pr(SL_FMC_Correct), predict()}{p_end}
{p2col:dy/dx wrt:}{res:MVC_Correct}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}MVC_Correct {c |}{col 14}{res}{space 2} .3269782{col 26}{space 2} .0278743{col 37}{space 1}   11.73{col 46}{space 3}0.000{col 54}{space 4} .2723457{col 67}{space 3} .3816107
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. 
. ****************
. ****************
. *QUALTRICS Study
. ****************
. ****************
. 
. use "Qualtrics_replicationdata.dta", clear
{txt}
{com}. 
. /*Note:  Qualtrics identifies individuals who completed the survey too quickly and excludes them
> from the total N count. These individuals are therefore excluded from the majority of analyses 
> below via specifying "if Speeder==0". However,as we report in the manuscript, we find that many 
> MVC non-passers were NOT identified by Qualtrics as "speeders": */
. tab Speeder MVC_Correct, column chi2 // relationship between "Speeder" status and MVC performance
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

  RECODE of {c |}      MVC_Correct
    gc (gc) {c |} Incorrect    Correct {c |}     Total
{hline 12}{c +}{hline 22}{c +}{hline 10}
Non-Speeder {c |}{res}       284        500 {txt}{c |}{res}       784 
            {txt}{c |}{res}     63.82      89.13 {txt}{c |}{res}     77.93 
{txt}{hline 12}{c +}{hline 22}{c +}{hline 10}
    Speeder {c |}{res}       161         61 {txt}{c |}{res}       222 
            {txt}{c |}{res}     36.18      10.87 {txt}{c |}{res}     22.07 
{txt}{hline 12}{c +}{hline 22}{c +}{hline 10}
      Total {c |}{res}       445        561 {txt}{c |}{res}     1,006 
            {txt}{c |}{res}    100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}1{txt}) = {res} 92.4082  {txt} Pr = {res}0.000
{txt}
{com}. 
. 
. reg expDV i.kkktreatment if Speeder==0 // Experiment ITT replication 

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,040
{txt}{hline 13}{c +}{hline 34}   F(1, 1038)      = {res}    14.77
{txt}       Model {c |} {res} 54.7317509         1  54.7317509   {txt}Prob > F        ={res}    0.0001
{txt}    Residual {c |} {res}  3845.4596     1,038  3.70468169   {txt}R-squared       ={res}    0.0140
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0131
{txt}       Total {c |} {res} 3900.19135     1,039   3.7537934   {txt}Root MSE        =   {res} 1.9248

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        expDV{col 15}{c |} Coefficient{col 27}  Std. err.{col 39}      t{col 47}   P>|t|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}kkktreatment {c |}
Public Order  {c |}{col 15}{res}{space 2}-.4588407{col 27}{space 2} .1193762{col 38}{space 1}   -3.84{col 47}{space 3}0.000{col 55}{space 4}-.6930868{col 68}{space 3}-.2245945
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 3.029183{col 27}{space 2} .0848973{col 38}{space 1}   35.68{col 47}{space 3}0.000{col 55}{space 4} 2.862593{col 68}{space 3} 3.195773
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg expDV i.kkktreatment if MVC_Correct==0 & Speeder==0 // Effect among MVC non-passers= -.36 (p=.10)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       284
{txt}{hline 13}{c +}{hline 34}   F(1, 282)       = {res}     2.66
{txt}       Model {c |} {res} 9.25879406         1  9.25879406   {txt}Prob > F        ={res}    0.1038
{txt}    Residual {c |} {res} 980.385572       282  3.47654458   {txt}R-squared       ={res}    0.0094
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0058
{txt}       Total {c |} {res} 989.644366       283  3.49697656   {txt}Root MSE        =   {res} 1.8645

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        expDV{col 15}{c |} Coefficient{col 27}  Std. err.{col 39}      t{col 47}   P>|t|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}kkktreatment {c |}
Public Order  {c |}{col 15}{res}{space 2}-.3616915{col 27}{space 2} .2216333{col 38}{space 1}   -1.63{col 47}{space 3}0.104{col 55}{space 4}-.7979572{col 68}{space 3} .0745741
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 2.828358{col 27}{space 2} .1610725{col 38}{space 1}   17.56{col 47}{space 3}0.000{col 55}{space 4} 2.511301{col 68}{space 3} 3.145415
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg expDV i.kkktreatment if MVC_Correct==1 & Speeder==0 // Effect among MVC passers = -.51 (p<.01)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       500
{txt}{hline 13}{c +}{hline 34}   F(1, 498)       = {res}     8.34
{txt}       Model {c |} {res} 32.4595207         1  32.4595207   {txt}Prob > F        ={res}    0.0040
{txt}    Residual {c |} {res} 1937.73848       498  3.89104112   {txt}R-squared       ={res}    0.0165
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0145
{txt}       Total {c |} {res}   1970.198       499  3.94829259   {txt}Root MSE        =   {res} 1.9726

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        expDV{col 15}{c |} Coefficient{col 27}  Std. err.{col 39}      t{col 47}   P>|t|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}kkktreatment {c |}
Public Order  {c |}{col 15}{res}{space 2}-.5096006{col 27}{space 2} .1764379{col 38}{space 1}   -2.89{col 47}{space 3}0.004{col 55}{space 4} -.856255{col 68}{space 3}-.1629463
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 3.154762{col 27}{space 2} .1242604{col 38}{space 1}   25.39{col 47}{space 3}0.000{col 55}{space 4} 2.910623{col 68}{space 3} 3.398901
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. ********************
. ** Table A6 Analyses
. ********************
. ci means MV_Time if Speeder==0 // mean time spent reading MV (with CI)

{txt}    Variable {c |}        Obs        Mean    Std. err.       [95% conf. interval]
{hline 13}{c +}{hline 63}
     MV_Time {c |}{col 16}{res}       784{col 29} 64.96446{col 41} 2.529765{col 57} 59.99854{col 69} 69.93039{txt}

{com}. proportion MVC_Correct if Speeder==0 // proportion passing MVC (64%)
{res}
{txt}{col 1}Proportion estimation{col 44}{lalign 13:Number of obs}{col 57} = {res}{ralign 3:784}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 37}             L{col 51}ogit
{col 14}{c |} Proportion{col 26}   Std. err.{col 38}     [95% con{col 51}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 1}MVC_Correct {c |}
{space 2}Incorrect  {c |}{col 14}{res}{space 2} .3622449{col 26}{space 2}  .017166{col 37}{space 5}  .329269{col 51}{space 3} .3965707
{txt}{space 4}Correct  {c |}{col 14}{res}{space 2} .6377551{col 26}{space 2}  .017166{col 37}{space 5} .6034293{col 51}{space 3}  .670731
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}.         * Among those not identified as "speeders" by Qualtrics
.         proportion MVC_Correct // 55%
{res}
{txt}{col 1}Proportion estimation{col 42}{lalign 13:Number of obs}{col 55} = {res}{ralign 5:1,140}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 37}             L{col 51}ogit
{col 14}{c |} Proportion{col 26}   Std. err.{col 38}     [95% con{col 51}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 1}MVC_Correct {c |}
{space 2}Incorrect  {c |}{col 14}{res}{space 2}  .445614{col 26}{space 2} .0147209{col 37}{space 5} .4169459{col 51}{space 3} .4746486
{txt}{space 4}Correct  {c |}{col 14}{res}{space 2}  .554386{col 26}{space 2} .0147209{col 37}{space 5} .5253514{col 51}{space 3} .5830541
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. 
. di .6377551-.2 // difference between passage rate above and pr(answering MVC correctly by chance)
{res}.4377551
{txt}
{com}. prtest MVC_Correct==.2 // significance test for above difference

{txt}One-sample test of proportion                   Number of obs      = {res}     1140

{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. err.{col 44}{col 49}{col 59}[95% conf. interval]
{hline 13}{c +}{hline 64}
{col 2}MVC_Correct{col 14}{c |}{res}{col 17}  .554386{col 28} .0147209{col 58} .5255336{col 70} .5832383
{txt}{hline 13}{c BT}{hline 64}
    p = proportion({res}MVC_Correct{txt})                                   z = {res} 29.9136
{txt}H0: p = {res}0.2

     {txt}Ha: p < {res}0.2                 {txt}Ha: p != {res}0.2                   {txt}Ha: p > {res}0.2
 {txt}Pr(Z < z) = {res}1.0000         {txt}Pr(|Z| > |z|) = {res}0.0000          {txt}Pr(Z > z) = {res}0.0000
{txt}
{com}. 
. ********************
. ** Table A7 Analyses
. ********************
. 
. bysort kkktreatment:  tab MVC_Correct if Speeder==0 // % passing n MVCs by experimental group

{txt}{hline}
-> kkktreatment = Free Speech

MVC_Correct {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
  Incorrect {c |}{res}        134       34.72       34.72
{txt}    Correct {c |}{res}        252       65.28      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        386      100.00

{txt}{hline}
-> kkktreatment = Public Order

MVC_Correct {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
  Incorrect {c |}{res}        150       37.69       37.69
{txt}    Correct {c |}{res}        248       62.31      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        398      100.00

{txt}{hline}
-> kkktreatment = .
no observations


{com}. 
. tab MVC_Correct if Speeder==0 // % passing n MVCs in overall sample

{txt}MVC_Correct {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
  Incorrect {c |}{res}        284       36.22       36.22
{txt}    Correct {c |}{res}        500       63.78      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        784      100.00
{txt}
{com}. 
. ********************
. *Table B1. Demographic Results (restricted to those featured in model)
. ********************
. reg expDV i.kkktreatment##i.MVC_Correct if Speeder==0 // regression model to calculate demographics

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       784
{txt}{hline 13}{c +}{hline 34}   F(3, 780)       = {res}     4.85
{txt}       Model {c |} {res} 54.4065608         3  18.1355203   {txt}Prob > F        ={res}    0.0024
{txt}    Residual {c |} {res} 2918.12405       780  3.74118468   {txt}R-squared       ={res}    0.0183
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0145
{txt}       Total {c |} {res} 2972.53061       783  3.79633539   {txt}Root MSE        =   {res} 1.9342

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                   expDV{col 26}{c |} Coefficient{col 38}  Std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}kkktreatment {c |}
{space 11}Public Order  {c |}{col 26}{res}{space 2}-.3616915{col 38}{space 2} .2299142{col 49}{space 1}   -1.57{col 58}{space 3}0.116{col 66}{space 4}-.8130153{col 79}{space 3} .0896322
{txt}{space 24} {c |}
{space 13}MVC_Correct {c |}
{space 16}Correct  {c |}{col 26}{res}{space 2} .3264037{col 38}{space 2} .2067976{col 49}{space 1}    1.58{col 58}{space 3}0.115{col 66}{space 4}-.0795421{col 79}{space 3} .7323495
{txt}{space 24} {c |}
kkktreatment#MVC_Correct {c |}
{space 3}Public Order#Correct  {c |}{col 26}{res}{space 2}-.1479091{col 38}{space 2} .2877358{col 49}{space 1}   -0.51{col 58}{space 3}0.607{col 66}{space 4}-.7127374{col 79}{space 3} .4169193
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2} 2.828358{col 38}{space 2} .1670907{col 49}{space 1}   16.93{col 58}{space 3}0.000{col 66}{space 4} 2.500358{col 79}{space 3} 3.156359
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
.         *Descriptive statistics for income, education, age and political interest
. tabstat income educ age polint if e(sample), st(mean median)

{txt}   Stats {...}
{c |}{...}
    income      educ       age    polint
{hline 9}{c +}{hline 40}
{ralign 8:Mean} {...}
{c |}{...}
 {res} 2.755102  3.274235   46.5625  3.438776
{txt}{ralign 8:p50} {...}
{c |}{...}
 {res}        2         3        47         4
{txt}{hline 9}{c BT}{hline 40}

{com}. 
. tab1 gender race_5cat pid7 ideology if e(sample) // % female, racial groups, party and ideology

{res}-> tabulation of gender if e(sample) 

     {txt}gender {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       Male {c |}{res}        392       50.00       50.00
{txt}     Female {c |}{res}        392       50.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        784      100.00

-> tabulation of race_5cat if e(sample) 

        {txt}RECODE of race (race) {c |}      Freq.     Percent        Cum.
{hline 30}{c +}{hline 35}
           Non-Hispanic White {c |}{res}        489       62.37       62.37
{txt}Non-Hispanic African-American {c |}{res}         93       11.86       74.23
{txt}                     Hispanic {c |}{res}        134       17.09       91.33
{txt}                        Asian {c |}{res}         45        5.74       97.07
{txt}                        Other {c |}{res}         23        2.93      100.00
{txt}{hline 30}{c +}{hline 35}
                        Total {c |}{res}        784      100.00

-> tabulation of pid7 if e(sample) 

             {txt}pid7 {c |}      Freq.     Percent        Cum.
{hline 18}{c +}{hline 35}
  Strong Democrat {c |}{res}        133       16.96       16.96
{txt}         Democrat {c |}{res}        163       20.79       37.76
{txt}    Lean Democrat {c |}{res}         70        8.93       46.68
{txt}      Independent {c |}{res}        151       19.26       65.94
{txt}  Lean Republican {c |}{res}         68        8.67       74.62
{txt}       Republican {c |}{res}        108       13.78       88.39
{txt}Strong Republican {c |}{res}         91       11.61      100.00
{txt}{hline 18}{c +}{hline 35}
            Total {c |}{res}        784      100.00

-> tabulation of ideology if e(sample) 

              {txt}ideology {c |}      Freq.     Percent        Cum.
{hline 23}{c +}{hline 35}
     Extremely Liberal {c |}{res}         76        9.69        9.69
{txt}               Liberal {c |}{res}        128       16.33       26.02
{txt}      Slightly Liberal {c |}{res}         78        9.95       35.97
{txt}              Moderate {c |}{res}        227       28.95       64.92
{txt} Slightly Conservative {c |}{res}         78        9.95       74.87
{txt}          Conservative {c |}{res}        129       16.45       91.33
{txt}Extremely Conservative {c |}{res}         68        8.67      100.00
{txt}{hline 23}{c +}{hline 35}
                 Total {c |}{res}        784      100.00
{txt}
{com}. 
. *Additional information
. tab1 gender race_5cat educ polint pid7 ideology income if e(sample)

{res}-> tabulation of gender if e(sample) 

     {txt}gender {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       Male {c |}{res}        392       50.00       50.00
{txt}     Female {c |}{res}        392       50.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        784      100.00

-> tabulation of race_5cat if e(sample) 

        {txt}RECODE of race (race) {c |}      Freq.     Percent        Cum.
{hline 30}{c +}{hline 35}
           Non-Hispanic White {c |}{res}        489       62.37       62.37
{txt}Non-Hispanic African-American {c |}{res}         93       11.86       74.23
{txt}                     Hispanic {c |}{res}        134       17.09       91.33
{txt}                        Asian {c |}{res}         45        5.74       97.07
{txt}                        Other {c |}{res}         23        2.93      100.00
{txt}{hline 30}{c +}{hline 35}
                        Total {c |}{res}        784      100.00

-> tabulation of educ if e(sample) 

                 {txt}educ {c |}      Freq.     Percent        Cum.
{hline 22}{c +}{hline 35}
                  <HS {c |}{res}         23        2.93        2.93
{txt}              HS grad {c |}{res}        183       23.34       26.28
{txt}         Some college {c |}{res}        246       31.38       57.65
{txt}         College grad {c |}{res}        240       30.61       88.27
{txt}              Masters {c |}{res}         72        9.18       97.45
{txt}PhD or other advanced {c |}{res}         20        2.55      100.00
{txt}{hline 22}{c +}{hline 35}
                Total {c |}{res}        784      100.00

-> tabulation of polint if e(sample) 

              {txt}polint {c |}      Freq.     Percent        Cum.
{hline 21}{c +}{hline 35}
      Not interested {c |}{res}         63        8.04        8.04
{txt}                   2 {c |}{res}        122       15.56       23.60
{txt}                   3 {c |}{res}        196       25.00       48.60
{txt}                   4 {c |}{res}        214       27.30       75.89
{txt}Extremely interested {c |}{res}        189       24.11      100.00
{txt}{hline 21}{c +}{hline 35}
               Total {c |}{res}        784      100.00

-> tabulation of pid7 if e(sample) 

             {txt}pid7 {c |}      Freq.     Percent        Cum.
{hline 18}{c +}{hline 35}
  Strong Democrat {c |}{res}        133       16.96       16.96
{txt}         Democrat {c |}{res}        163       20.79       37.76
{txt}    Lean Democrat {c |}{res}         70        8.93       46.68
{txt}      Independent {c |}{res}        151       19.26       65.94
{txt}  Lean Republican {c |}{res}         68        8.67       74.62
{txt}       Republican {c |}{res}        108       13.78       88.39
{txt}Strong Republican {c |}{res}         91       11.61      100.00
{txt}{hline 18}{c +}{hline 35}
            Total {c |}{res}        784      100.00

-> tabulation of ideology if e(sample) 

              {txt}ideology {c |}      Freq.     Percent        Cum.
{hline 23}{c +}{hline 35}
     Extremely Liberal {c |}{res}         76        9.69        9.69
{txt}               Liberal {c |}{res}        128       16.33       26.02
{txt}      Slightly Liberal {c |}{res}         78        9.95       35.97
{txt}              Moderate {c |}{res}        227       28.95       64.92
{txt} Slightly Conservative {c |}{res}         78        9.95       74.87
{txt}          Conservative {c |}{res}        129       16.45       91.33
{txt}Extremely Conservative {c |}{res}         68        8.67      100.00
{txt}{hline 23}{c +}{hline 35}
                 Total {c |}{res}        784      100.00

-> tabulation of income if e(sample) 

     {txt}income {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
      0-25k {c |}{res}        196       25.00       25.00
{txt}    25k-50k {c |}{res}        209       26.66       51.66
{txt}    50k-75k {c |}{res}        147       18.75       70.41
{txt}    75-100k {c |}{res}        112       14.29       84.69
{txt}  100k-150k {c |}{res}         82       10.46       95.15
{txt}   150-200k {c |}{res}         19        2.42       97.58
{txt}  Over 200k {c |}{res}         19        2.42      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        784      100.00
{txt}
{com}. 
. ********************
. *Table D1 Analyses
. ********************
. 
. *Demographic predictors of MVC performance
. reg MVC_Correct i.gender i.race_5cat age_01 income_01 educ_01 polint_01 pid7_01 ideology_01 if Speeder==0 

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       784
{txt}{hline 13}{c +}{hline 34}   F(11, 772)      = {res}     5.33
{txt}       Model {c |} {res} 12.7749401        11  1.16135819   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 168.347509       772  .218066721   {txt}R-squared       ={res}    0.0705
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0573
{txt}       Total {c |} {res} 181.122449       783  .231318581   {txt}Root MSE        =   {res} .46698

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                MVC_Correct{col 29}{c |} Coefficient{col 41}  Std. err.{col 53}      t{col 61}   P>|t|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}gender {c |}
{space 20}Female  {c |}{col 29}{res}{space 2} .0630001{col 41}{space 2} .0362779{col 52}{space 1}    1.74{col 61}{space 3}0.083{col 69}{space 4} -.008215{col 82}{space 3} .1342152
{txt}{space 27} {c |}
{space 18}race_5cat {c |}
Non-Hispanic African-Ame..  {c |}{col 29}{res}{space 2} -.128867{col 41}{space 2} .0584052{col 52}{space 1}   -2.21{col 61}{space 3}0.028{col 69}{space 4}-.2435189{col 82}{space 3}-.0142151
{txt}{space 18}Hispanic  {c |}{col 29}{res}{space 2}-.0211212{col 41}{space 2} .0504386{col 52}{space 1}   -0.42{col 61}{space 3}0.676{col 69}{space 4}-.1201342{col 82}{space 3} .0778919
{txt}{space 21}Asian  {c |}{col 29}{res}{space 2}-.1319311{col 41}{space 2} .0756817{col 52}{space 1}   -1.74{col 61}{space 3}0.082{col 69}{space 4}-.2804974{col 82}{space 3} .0166352
{txt}{space 21}Other  {c |}{col 29}{res}{space 2}-.0154714{col 41}{space 2} .1024803{col 52}{space 1}   -0.15{col 61}{space 3}0.880{col 69}{space 4}-.2166444{col 82}{space 3} .1857016
{txt}{space 27} {c |}
{space 21}age_01 {c |}{col 29}{res}{space 2} .4818627{col 41}{space 2} .0926039{col 52}{space 1}    5.20{col 61}{space 3}0.000{col 69}{space 4} .3000774{col 82}{space 3} .6636481
{txt}{space 18}income_01 {c |}{col 29}{res}{space 2}-.0423253{col 41}{space 2} .0770566{col 52}{space 1}   -0.55{col 61}{space 3}0.583{col 69}{space 4}-.1935906{col 82}{space 3}   .10894
{txt}{space 20}educ_01 {c |}{col 29}{res}{space 2} .1066357{col 41}{space 2} .0913406{col 52}{space 1}    1.17{col 61}{space 3}0.243{col 69}{space 4}-.0726696{col 82}{space 3} .2859411
{txt}{space 18}polint_01 {c |}{col 29}{res}{space 2} .0753445{col 41}{space 2} .0576652{col 52}{space 1}    1.31{col 61}{space 3}0.192{col 69}{space 4}-.0378548{col 82}{space 3} .1885437
{txt}{space 20}pid7_01 {c |}{col 29}{res}{space 2}-.0174871{col 41}{space 2} .0642495{col 52}{space 1}   -0.27{col 61}{space 3}0.786{col 69}{space 4}-.1436115{col 82}{space 3} .1086374
{txt}{space 16}ideology_01 {c |}{col 29}{res}{space 2}-.0945371{col 41}{space 2} .0719126{col 52}{space 1}   -1.31{col 61}{space 3}0.189{col 69}{space 4}-.2357045{col 82}{space 3} .0466303
{txt}{space 22}_cons {c |}{col 29}{res}{space 2} .4293527{col 41}{space 2} .0716611{col 52}{space 1}    5.99{col 61}{space 3}0.000{col 69}{space 4}  .288679{col 82}{space 3} .5700264
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
.         *Add above results to Table D2
. outreg2 using TableD1.doc, append ctitle(Qualtrics) dec(2) e(r2_a) ///
> alpha(0.001, 0.01, 0.05, .10) symbol(***, **, *,^) 
{txt}{stata `"shellout using `"TableD1.doc"'"':TableD1.doc}
{browse `"/Users/JohnVKane/Desktop/Analyze Attentive R&R/Replication Files/PSRM STATA Replication Files"' :dir}{com} : {txt}{stata `"seeout using "TableD1.txt""':seeout}

{com}. 
. *Other information Reported in Appendix D
. 
. *Correlations between race, age, and MVC performance
. tab race_5cat if e(sample), gen(race_dummy) // generate race dummy variables

        {txt}RECODE of race (race) {c |}      Freq.     Percent        Cum.
{hline 30}{c +}{hline 35}
           Non-Hispanic White {c |}{res}        489       62.37       62.37
{txt}Non-Hispanic African-American {c |}{res}         93       11.86       74.23
{txt}                     Hispanic {c |}{res}        134       17.09       91.33
{txt}                        Asian {c |}{res}         45        5.74       97.07
{txt}                        Other {c |}{res}         23        2.93      100.00
{txt}{hline 30}{c +}{hline 35}
                        Total {c |}{res}        784      100.00
{txt}
{com}. 
.         *Correlations between MVC performance, age, and race dummy variables
. pwcorr MVC_Correct age race_dummy1-race_dummy5 if e(sample), sig 

             {txt}{c |} MVC_Co~t      age race_d~1 race_d~2 race_d~3 race_d~4 race_d~5
{hline 13}{c +}{hline 63}
 MVC_Correct {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
         age {c |} {res}  0.2268   1.0000 
             {txt}{c |}{res}   0.0000
             {txt}{c |}
 race_dummy1 {c |} {res}  0.1541   0.4890   1.0000 
             {txt}{c |}{res}   0.0000   0.0000
             {txt}{c |}
 race_dummy2 {c |} {res} -0.1092  -0.2046  -0.4723   1.0000 
             {txt}{c |}{res}   0.0022   0.0000   0.0000
             {txt}{c |}
 race_dummy3 {c |} {res} -0.0455  -0.3068  -0.5846  -0.1666   1.0000 
             {txt}{c |}{res}   0.2028   0.0000   0.0000   0.0000
             {txt}{c |}
 race_dummy4 {c |} {res} -0.0764  -0.1455  -0.3177  -0.0905  -0.1120   1.0000 
             {txt}{c |}{res}   0.0324   0.0000   0.0000   0.0112   0.0017
             {txt}{c |}
 race_dummy5 {c |} {res} -0.0262  -0.1268  -0.2238  -0.0638  -0.0789  -0.0429   1.0000 
             {txt}{c |}{res}   0.4632   0.0004   0.0000   0.0743   0.0271   0.2302
             {txt}{c |}

{com}. 
. *Original Model 
. reg expDV i.kkktreatment if Speeder==0 

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,040
{txt}{hline 13}{c +}{hline 34}   F(1, 1038)      = {res}    14.77
{txt}       Model {c |} {res} 54.7317509         1  54.7317509   {txt}Prob > F        ={res}    0.0001
{txt}    Residual {c |} {res}  3845.4596     1,038  3.70468169   {txt}R-squared       ={res}    0.0140
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0131
{txt}       Total {c |} {res} 3900.19135     1,039   3.7537934   {txt}Root MSE        =   {res} 1.9248

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        expDV{col 15}{c |} Coefficient{col 27}  Std. err.{col 39}      t{col 47}   P>|t|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}kkktreatment {c |}
Public Order  {c |}{col 15}{res}{space 2}-.4588407{col 27}{space 2} .1193762{col 38}{space 1}   -3.84{col 47}{space 3}0.000{col 55}{space 4}-.6930868{col 68}{space 3}-.2245945
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 3.029183{col 27}{space 2} .0848973{col 38}{space 1}   35.68{col 47}{space 3}0.000{col 55}{space 4} 2.862593{col 68}{space 3} 3.195773
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. tab race_5cat if e(sample) // race demographics for sample

        {txt}RECODE of race (race) {c |}      Freq.     Percent        Cum.
{hline 30}{c +}{hline 35}
           Non-Hispanic White {c |}{res}        648       62.31       62.31
{txt}Non-Hispanic African-American {c |}{res}        123       11.83       74.13
{txt}                     Hispanic {c |}{res}        180       17.31       91.44
{txt}                        Asian {c |}{res}         56        5.38       96.83
{txt}                        Other {c |}{res}         33        3.17      100.00
{txt}{hline 30}{c +}{hline 35}
                        Total {c |}{res}      1,040      100.00
{txt}
{com}. sum age if e(sample) // age statistics for sample

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 9}age {c |}{res}      1,040    46.43942    17.12402         18         96
{txt}
{com}. 
. *Model Subsetting on Passing the MVC
. reg expDV i.kkktreatment if Speeder==0 & MVC_Correct==1 

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       500
{txt}{hline 13}{c +}{hline 34}   F(1, 498)       = {res}     8.34
{txt}       Model {c |} {res} 32.4595207         1  32.4595207   {txt}Prob > F        ={res}    0.0040
{txt}    Residual {c |} {res} 1937.73848       498  3.89104112   {txt}R-squared       ={res}    0.0165
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0145
{txt}       Total {c |} {res}   1970.198       499  3.94829259   {txt}Root MSE        =   {res} 1.9726

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        expDV{col 15}{c |} Coefficient{col 27}  Std. err.{col 39}      t{col 47}   P>|t|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}kkktreatment {c |}
Public Order  {c |}{col 15}{res}{space 2}-.5096006{col 27}{space 2} .1764379{col 38}{space 1}   -2.89{col 47}{space 3}0.004{col 55}{space 4} -.856255{col 68}{space 3}-.1629463
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 3.154762{col 27}{space 2} .1242604{col 38}{space 1}   25.39{col 47}{space 3}0.000{col 55}{space 4} 2.910623{col 68}{space 3} 3.398901
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. tab race_5cat if e(sample) //race demographics for sample of MVC passers

        {txt}RECODE of race (race) {c |}      Freq.     Percent        Cum.
{hline 30}{c +}{hline 35}
           Non-Hispanic White {c |}{res}        340       68.00       68.00
{txt}Non-Hispanic African-American {c |}{res}         46        9.20       77.20
{txt}                     Hispanic {c |}{res}         79       15.80       93.00
{txt}                        Asian {c |}{res}         22        4.40       97.40
{txt}                        Other {c |}{res}         13        2.60      100.00
{txt}{hline 30}{c +}{hline 35}
                        Total {c |}{res}        500      100.00
{txt}
{com}. sum age if e(sample) // age statistics for sample of MVC passers

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 9}age {c |}{res}        500      49.496    17.10506         18         96
{txt}
{com}. 
. 
. ********************
. *Table E1 Analyses
. ********************
. 
. *Better MVC Performance Predicts Greater Time Spent
. 
. **Predicting time spent on Mock Vignette
. reg logged_Q10_pageSubmit i.MVC_Correct if Speeder==0 // (83% increase)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       784
{txt}{hline 13}{c +}{hline 34}   F(1, 782)       = {res}    99.82
{txt}       Model {c |} {res}  124.90195         1   124.90195   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res}  978.46221       782  1.25123045   {txt}R-squared       ={res}    0.1132
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1121
{txt}       Total {c |} {res} 1103.36416       783  1.40914963   {txt}Root MSE        =   {res} 1.1186

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}logged_Q10~t{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}MVC_Correct {c |}
{space 4}Correct  {c |}{col 14}{res}{space 2} .8304213{col 26}{space 2} .0831156{col 37}{space 1}    9.99{col 46}{space 3}0.000{col 54}{space 4} .6672652{col 67}{space 3} .9935774
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.125683{col 26}{space 2} .0663758{col 37}{space 1}   47.09{col 46}{space 3}0.000{col 54}{space 4} 2.995387{col 67}{space 3} 3.255979
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. **Predicting time Spent on Control Vignette (Free Speech)
. reg logged_Q54_PageSubmit i.MVC_Correct if Speeder==0 // (88% increase)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       386
{txt}{hline 13}{c +}{hline 34}   F(1, 384)       = {res}    56.45
{txt}       Model {c |} {res}  67.358883         1   67.358883   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 458.199237       384  1.19322718   {txt}R-squared       ={res}    0.1282
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1259
{txt}       Total {c |} {res}  525.55812       385  1.36508603   {txt}Root MSE        =   {res} 1.0923

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}logged_Q54~t{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}MVC_Correct {c |}
{space 4}Correct  {c |}{col 14}{res}{space 2} .8774824{col 26}{space 2} .1167892{col 37}{space 1}    7.51{col 46}{space 3}0.000{col 54}{space 4} .6478561{col 67}{space 3} 1.107109
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.015849{col 26}{space 2} .0943646{col 37}{space 1}   31.96{col 46}{space 3}0.000{col 54}{space 4} 2.830313{col 67}{space 3} 3.201385
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. **Predicting time Spent on Treatment Vignette (Public Order)
. reg logged_Q12_PageSubmit i.MVC_Correct if Speeder==0 // (81% increase)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       398
{txt}{hline 13}{c +}{hline 34}   F(1, 396)       = {res}    45.45
{txt}       Model {c |} {res} 61.2899302         1  61.2899302   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 534.010076       396  1.34851029   {txt}R-squared       ={res}    0.1030
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1007
{txt}       Total {c |} {res} 595.300006       397  1.49949624   {txt}Root MSE        =   {res} 1.1613

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}logged_Q12~t{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}MVC_Correct {c |}
{space 4}Correct  {c |}{col 14}{res}{space 2} .8097755{col 26}{space 2}  .120115{col 37}{space 1}    6.74{col 46}{space 3}0.000{col 54}{space 4} .5736327{col 67}{space 3} 1.045918
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.043868{col 26}{space 2}  .094816{col 37}{space 1}   32.10{col 46}{space 3}0.000{col 54}{space 4} 2.857463{col 67}{space 3} 3.230274
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. **Predicting time spent on outcome measure
. reg Q11_logged i.MVC_Correct if Speeder==0 // (12% increase)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       784
{txt}{hline 13}{c +}{hline 34}   F(1, 782)       = {res}     6.28
{txt}       Model {c |} {res} 2.61348317         1  2.61348317   {txt}Prob > F        ={res}    0.0124
{txt}    Residual {c |} {res} 325.489182       782  .416226576   {txt}R-squared       ={res}    0.0080
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0067
{txt}       Total {c |} {res} 328.102665       783  .419032778   {txt}Root MSE        =   {res} .64516

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  Q11_logged{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}MVC_Correct {c |}
{space 4}Correct  {c |}{col 14}{res}{space 2} .1201223{col 26}{space 2} .0479379{col 37}{space 1}    2.51{col 46}{space 3}0.012{col 54}{space 4} .0260202{col 67}{space 3} .2142245
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.132704{col 26}{space 2}  .038283{col 37}{space 1}   55.71{col 46}{space 3}0.000{col 54}{space 4} 2.057554{col 67}{space 3} 2.207853
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. **Predicting time spent on entire survey
. reg log_Durationinseconds i.MVC_Correct if Speeder==0 // (10% increase)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       784
{txt}{hline 13}{c +}{hline 34}   F(1, 782)       = {res}     5.22
{txt}       Model {c |} {res} 1.63615403         1  1.63615403   {txt}Prob > F        ={res}    0.0226
{txt}    Residual {c |} {res} 245.246866       782  .313614918   {txt}R-squared       ={res}    0.0066
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0054
{txt}       Total {c |} {res}  246.88302       783  .315303985   {txt}Root MSE        =   {res} .56001

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}log_Durati~s{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}MVC_Correct {c |}
{space 4}Correct  {c |}{col 14}{res}{space 2} .0950443{col 26}{space 2} .0416114{col 37}{space 1}    2.28{col 46}{space 3}0.023{col 54}{space 4}  .013361{col 67}{space 3} .1767275
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  6.78882{col 26}{space 2} .0332307{col 37}{space 1}  204.29{col 46}{space 3}0.000{col 54}{space 4} 6.723588{col 67}{space 3} 6.854052
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. **Predicting passage of Factual Manipulation Check
. logit FMC_Correct i.MVC_Correct if Speeder==0 // 

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-495.96943}  
Iteration 1:{space 3}log likelihood = {res:-447.62602}  
Iteration 2:{space 3}log likelihood = {res:-447.05272}  
Iteration 3:{space 3}log likelihood = {res:-447.05254}  
Iteration 4:{space 3}log likelihood = {res:-447.05254}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:784}
{txt}{col 57}{lalign 13:LR chi2({res:1})}{col 70} = {res}{ralign 6:97.83}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-447.05254}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0986}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} FMC_Correct{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}MVC_Correct {c |}
{space 4}Correct  {c |}{col 14}{res}{space 2} 1.571667{col 26}{space 2}  .163187{col 37}{space 1}    9.63{col 46}{space 3}0.000{col 54}{space 4} 1.251826{col 67}{space 3} 1.891507
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.1978257{col 26}{space 2} .1192592{col 37}{space 1}   -1.66{col 46}{space 3}0.097{col 54}{space 4}-.4315695{col 67}{space 3}  .035918
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, dydx(MVC_Correct) // effect of MVC performance on pr(answering FMC correctly)=.35
{res}
{txt}{col 1}Conditional marginal effects{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:784}
{txt}{col 1}Model VCE: {res:OIM}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Pr(FMC_Correct), predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.MVC_Correct}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}MVC_Correct {c |}
{space 4}Correct  {c |}{col 14}{res}{space 2} .3472958{col 26}{space 2}  .034556{col 37}{space 1}   10.05{col 46}{space 3}0.000{col 54}{space 4} .2795673{col 67}{space 3} .4150243
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. 
. ********************
. *Appendix G
. ********************
. *Proportion of sample randomly assigned to not receive an MV
. proportion No_MV_Shown if Speeder==0
{res}
{txt}{col 1}Proportion estimation{col 42}{lalign 13:Number of obs}{col 55} = {res}{ralign 5:1,040}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 37}             L{col 51}ogit
{col 14}{c |} Proportion{col 26}   Std. err.{col 38}     [95% con{col 51}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 1}No_MV_Shown {c |}
{space 3}MV Shown  {c |}{col 14}{res}{space 2} .7538462{col 26}{space 2} .0133576{col 37}{space 5} .7267072{col 51}{space 3} .7791092
{txt}No MV Shown  {c |}{col 14}{res}{space 2} .2461538{col 26}{space 2} .0133576{col 37}{space 5} .2208908{col 51}{space 3} .2732928
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. 
. 
. 
. *********************
. *********************
. *LUCID STUDY 1
. *********************
. *********************
. 
. use "Lucid1_replicationdata.dta", clear
{txt}
{com}. 
. *Replicate each experiment's ITT
. reg slexpdv i.SLexpTreatment // Student Loan Experiment/MockVignette_MTurk

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     2,755
{txt}{hline 13}{c +}{hline 34}   F(1, 2753)      = {res}    99.01
{txt}       Model {c |} {res} 459.828729         1  459.828729   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 12785.6798     2,753  4.64427163   {txt}R-squared       ={res}    0.0347
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0344
{txt}       Total {c |} {res} 13245.5085     2,754  4.80955284   {txt}Root MSE        =   {res} 2.1551

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         slexpdv{col 18}{c |} Coefficient{col 30}  Std. err.{col 42}      t{col 50}   P>|t|{col 58}     [95% con{col 71}f. interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
1.SLexpTreatment {c |}{col 18}{res}{space 2}-.8170855{col 30}{space 2} .0821161{col 41}{space 1}   -9.95{col 50}{space 3}0.000{col 58}{space 4}-.9781009{col 71}{space 3}-.6560701
{txt}{space 11}_cons {c |}{col 18}{res}{space 2} 4.563452{col 30}{space 2} .0580332{col 41}{space 1}   78.64{col 50}{space 3}0.000{col 58}{space 4} 4.449659{col 71}{space 3} 4.677245
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg kkkexpdv i.KKKexpPO // KKK Framing Study

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     2,729
{txt}{hline 13}{c +}{hline 34}   F(1, 2727)      = {res}   108.91
{txt}       Model {c |} {res} 397.894562         1  397.894562   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res}  9963.0032     2,727  3.65346652   {txt}R-squared       ={res}    0.0384
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0381
{txt}       Total {c |} {res} 10360.8978     2,728  3.79798305   {txt}Root MSE        =   {res} 1.9114

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}    kkkexpdv{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}1.KKKexpPO {c |}{col 14}{res}{space 2}-.7636815{col 26}{space 2}  .073178{col 37}{space 1}  -10.44{col 46}{space 3}0.000{col 54}{space 4}-.9071715{col 67}{space 3}-.6201915
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.338462{col 26}{space 2} .0517352{col 37}{space 1}   64.53{col 46}{space 3}0.000{col 54}{space 4} 3.237017{col 67}{space 3} 3.439906
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg apexpdv i.APexpLazy // Welfare Deservingness Study

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     2,742
{txt}{hline 13}{c +}{hline 34}   F(1, 2740)      = {res}   977.49
{txt}       Model {c |} {res} 3022.67775         1  3022.67775   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 8472.83538     2,740  3.09227569   {txt}R-squared       ={res}    0.2629
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.2627
{txt}       Total {c |} {res} 11495.5131     2,741  4.19391212   {txt}Root MSE        =   {res} 1.7585

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     apexpdv{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}1.APexpLazy {c |}{col 14}{res}{space 2} 2.099878{col 26}{space 2} .0671641{col 37}{space 1}   31.26{col 46}{space 3}0.000{col 54}{space 4} 1.968181{col 67}{space 3} 2.231575
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.208486{col 26}{space 2} .0475614{col 37}{space 1}   67.46{col 46}{space 3}0.000{col 54}{space 4} 3.115226{col 67}{space 3} 3.301746
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg DVsum_immexp01 i.IMMIGexpHSM // Immigration Study

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     2,743
{txt}{hline 13}{c +}{hline 34}   F(1, 2741)      = {res}    86.80
{txt}       Model {c |} {res} 4.59143572         1  4.59143572   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 144.988484     2,741    .0528962   {txt}R-squared       ={res}    0.0307
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0303
{txt}       Total {c |} {res} 149.579919     2,742  .054551393   {txt}Root MSE        =   {res} .22999

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}DVsum_imme~01{col 15}{c |} Coefficient{col 27}  Std. err.{col 39}      t{col 47}   P>|t|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
1.IMMIGexpHSM {c |}{col 15}{res}{space 2} .0818261{col 27}{space 2} .0087827{col 38}{space 1}    9.32{col 47}{space 3}0.000{col 55}{space 4} .0646046{col 68}{space 3} .0990475
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} .5926095{col 27}{space 2} .0062137{col 38}{space 1}   95.37{col 47}{space 3}0.000{col 55}{space 4} .5804254{col 68}{space 3} .6047935
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. *Grand correlation between first and second round MV performance
. pwcorr R1MVperformance R2MVperformance, sig obs // p=.594

             {txt}{c |} R1MVpe~e R2MVpe~e
{hline 13}{c +}{hline 18}
R1MVperfor~e {c |} {res}  1.0000 
             {txt}{c |}
             {c |}{res}     4465
             {txt}{c |}
R2MVperfor~e {c |} {res}  0.5940   1.0000 
             {txt}{c |}{res}   0.0000
             {txt}{c |}{res}     4325     5440
             {txt}{c |}

{com}. 
. *Pairwise correlations within each MVC scale
.         *MV #1:  Scientific Publishing
. pwcorr mvcheck1a_Correct mvcheck1b_Correct mvcheck1c_Correct, sig

             {txt}{c |} mvchec.. mvchec.. mvchec..
{hline 13}{c +}{hline 27}
mvcheck1a_~t {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
mvcheck1b_~t {c |} {res}  0.2977   1.0000 
             {txt}{c |}{res}   0.0000
             {txt}{c |}
mvcheck1c_~t {c |} {res}  0.4295   0.2915   1.0000 
             {txt}{c |}{res}   0.0000   0.0000
             {txt}{c |}

{com}.         
.         *MV #2:  Stadium Licenses
. pwcorr mvcheck2a_Correct mvcheck2b_Correct mvcheck2c_Correct, sig 

             {txt}{c |} mvchec.. mvchec.. mvchec..
{hline 13}{c +}{hline 27}
mvcheck2a_~t {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
mvcheck2b_~t {c |} {res}  0.6502   1.0000 
             {txt}{c |}{res}   0.0000
             {txt}{c |}
mvcheck2c_~t {c |} {res}  0.4208   0.4190   1.0000 
             {txt}{c |}{res}   0.0000   0.0000
             {txt}{c |}

{com}. 
.         *MV #3:  Sulfur Reductions
. pwcorr mvcheck3a_Correct mvcheck3b_Correct mvcheck3c_Correct, sig

             {txt}{c |} mvchec.. mvchec.. mvchec..
{hline 13}{c +}{hline 27}
mvcheck3a_~t {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
mvcheck3b_~t {c |} {res}  0.3856   1.0000 
             {txt}{c |}{res}   0.0000
             {txt}{c |}
mvcheck3c_~t {c |} {res}  0.4590   0.3792   1.0000 
             {txt}{c |}{res}   0.0000   0.0000
             {txt}{c |}

{com}. 
.         *MV #4:  Plant Removal
. pwcorr mvcheck4a_Correct mvcheck4b_Correct mvcheck4c_Correct, sig

             {txt}{c |} mvchec.. mvchec.. mvchec..
{hline 13}{c +}{hline 27}
mvcheck4a_~t {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
mvcheck4b_~t {c |} {res}  0.4084   1.0000 
             {txt}{c |}{res}   0.0000
             {txt}{c |}
mvcheck4c_~t {c |} {res}  0.3983   0.3769   1.0000 
             {txt}{c |}{res}   0.0000   0.0000
             {txt}{c |}

{com}. 
. 
. 
. *Cronbach's alpha values within each MVC scale
.         *MV #1:  Scientific Publishing
. alpha mvcheck1a_Correct mvcheck1b_Correct mvcheck1c_Correct, item

{txt}Test scale = mean(unstandardized items)

                                                            Average
                             Item-test     Item-rest       interitem
Item         {c |}  Obs  Sign   correlation   correlation     covariance      alpha
{hline 13}{c +}{hline 65}
mvcheck1a_~t{col 14}{c |}{res}{col 16}2497{col 24}+{col 31} 0.7431{col 45} 0.4438{col 59} .0663748{col 73} 0.4498
{txt}mvcheck1b_~t{col 14}{c |}{res}{col 16}2486{col 24}+{col 31} 0.7398{col 45} 0.3456{col 59} .0811061{col 73} 0.5990
{txt}mvcheck1c_~t{col 14}{c |}{res}{col 16}2470{col 24}+{col 31} 0.7607{col 45} 0.4380{col 59} .0616821{col 73} 0.4523
{txt}{hline 13}{c +}{hline 65}
Test scale{col 14}{c |}{res}{col 59} .0697037{col 73} 0.5988
{txt}{hline 13}{c BT}{hline 65}

{com}.         
.         *MV #2:  Stadium Licenses
. alpha mvcheck2a_Correct mvcheck2b_Correct mvcheck2c_Correct, item 

{txt}Test scale = mean(unstandardized items)

                                                            Average
                             Item-test     Item-rest       interitem
Item         {c |}  Obs  Sign   correlation   correlation     covariance      alpha
{hline 13}{c +}{hline 65}
mvcheck2a_~t{col 14}{c |}{res}{col 16}2497{col 24}+{col 31} 0.8407{col 45} 0.6232{col 59} .0828949{col 73} 0.5840
{txt}mvcheck2b_~t{col 14}{c |}{res}{col 16}2484{col 24}+{col 31} 0.8283{col 45} 0.6271{col 59} .0892094{col 73} 0.5886
{txt}mvcheck2c_~t{col 14}{c |}{res}{col 16}2476{col 24}+{col 31} 0.7774{col 45} 0.4624{col 59} .1158561{col 73} 0.7851
{txt}{hline 13}{c +}{hline 65}
Test scale{col 14}{c |}{res}{col 59} .0960082{col 73} 0.7384
{txt}{hline 13}{c BT}{hline 65}

{com}. 
.         *MV #3:  Sulfur Reductions
. alpha mvcheck3a_Correct mvcheck3b_Correct mvcheck3c_Correct, item

{txt}Test scale = mean(unstandardized items)

                                                            Average
                             Item-test     Item-rest       interitem
Item         {c |}  Obs  Sign   correlation   correlation     covariance      alpha
{hline 13}{c +}{hline 65}
mvcheck3a_~t{col 14}{c |}{res}{col 16}2488{col 24}+{col 31} 0.7631{col 45} 0.5068{col 59} .0871079{col 73} 0.5494
{txt}mvcheck3b_~t{col 14}{c |}{res}{col 16}2472{col 24}+{col 31} 0.7765{col 45} 0.4459{col 59} .0863551{col 73} 0.6226
{txt}mvcheck3c_~t{col 14}{c |}{res}{col 16}2466{col 24}+{col 31} 0.7945{col 45} 0.4976{col 59} .0750846{col 73} 0.5482
{txt}{hline 13}{c +}{hline 65}
Test scale{col 14}{c |}{res}{col 59} .0828429{col 73} 0.6676
{txt}{hline 13}{c BT}{hline 65}

{com}. 
.         *MV #4:  Plant Removal
. alpha mvcheck4a_Correct mvcheck4b_Correct mvcheck4c_Correct, item

{txt}Test scale = mean(unstandardized items)

                                                            Average
                             Item-test     Item-rest       interitem
Item         {c |}  Obs  Sign   correlation   correlation     covariance      alpha
{hline 13}{c +}{hline 65}
mvcheck4a_~t{col 14}{c |}{res}{col 16}2512{col 24}+{col 31} 0.7426{col 45} 0.4863{col 59}  .092535{col 73} 0.5474
{txt}mvcheck4b_~t{col 14}{c |}{res}{col 16}2497{col 24}+{col 31} 0.7877{col 45} 0.4659{col 59} .0770514{col 73} 0.5568
{txt}mvcheck4c_~t{col 14}{c |}{res}{col 16}2493{col 24}+{col 31} 0.7853{col 45} 0.4591{col 59} .0787528{col 73} 0.5678
{txt}{hline 13}{c +}{hline 65}
Test scale{col 14}{c |}{res}{col 59} .0827776{col 73} 0.6532
{txt}{hline 13}{c BT}{hline 65}

{com}. 
. 
. *APPENDIX A
. 
.         *TABLE A2: Scientific Publishing Vignette (Lucid Results)
. ci means q10_pagesubmit // mean time spent (and CI) on MV

{txt}    Variable {c |}        Obs        Mean    Std. err.       [95% conf. interval]
{hline 13}{c +}{hline 63}
q10_pagesu~t {c |}{col 16}{res}     2,548{col 29} 56.93308{col 41} 3.176531{col 57} 50.70424{col 69} 63.16193{txt}

{com}. 
. proportion mvcheck1a_Correct // proportion answering MVC correctly
{res}
{txt}{col 1}Proportion estimation{col 47}{lalign 13:Number of obs}{col 60} = {res}{ralign 5:2,497}

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 19}{c |}{col 42}             L{col 56}ogit
{col 19}{c |} Proportion{col 31}   Std. err.{col 43}     [95% con{col 56}f. interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
mvcheck1a_Correct {c |}
{space 7}Incorrect  {c |}{col 19}{res}{space 2} .2210653{col 31}{space 2} .0083043{col 42}{space 5} .2052113{col 56}{space 3} .2377776
{txt}{space 9}Correct  {c |}{col 19}{res}{space 2} .7789347{col 31}{space 2} .0083043{col 42}{space 5} .7622224{col 56}{space 3} .7947887
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. di .7789347-.16666667 // difference b/w above and answering correctly by chance
{res}.61226803
{txt}
{com}. prtest mvcheck1a_Correct==.16666667 // significance test

{txt}One-sample test of proportion                   Number of obs      = {res}     2497

{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. err.{col 44}{col 49}{col 59}[95% conf. interval]
{hline 13}{c +}{hline 64}
{col 1}mvcheck1a_~t{col 14}{c |}{res}{col 17} .7789347{col 28} .0083043{col 58} .7626586{col 70} .7952108
{txt}{hline 13}{c BT}{hline 64}
    p = proportion({res}mvcheck1a_~t{txt})                                  z = {res} 82.0951
{txt}H0: p = {res}0.166667

  {txt}Ha: p < {res}0.166667             {txt}Ha: p != {res}0.166667             {txt}Ha: p > {res}0.166667
 {txt}Pr(Z < z) = {res}1.0000         {txt}Pr(|Z| > |z|) = {res}0.0000          {txt}Pr(Z > z) = {res}0.0000
{txt}
{com}. 
. proportion mvcheck1b_Correct // proportion answering MVC correctly
{res}
{txt}{col 1}Proportion estimation{col 47}{lalign 13:Number of obs}{col 60} = {res}{ralign 5:2,486}

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 19}{c |}{col 42}             L{col 56}ogit
{col 19}{c |} Proportion{col 31}   Std. err.{col 43}     [95% con{col 56}f. interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
mvcheck1b_Correct {c |}
{space 7}Incorrect  {c |}{col 19}{res}{space 2} .5004023{col 31}{space 2} .0100281{col 42}{space 5} .4807474{col 56}{space 3} .5200558
{txt}{space 9}Correct  {c |}{col 19}{res}{space 2} .4995977{col 31}{space 2} .0100281{col 42}{space 5} .4799442{col 56}{space 3} .5192526
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. di .4995977-.16666667 // difference b/w above and answering correctly by chance
{res}.33293103
{txt}
{com}. prtest mvcheck1b_Correct==.16666667 // significance test

{txt}One-sample test of proportion                   Number of obs      = {res}     2486

{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. err.{col 44}{col 49}{col 59}[95% conf. interval]
{hline 13}{c +}{hline 64}
{col 1}mvcheck1b_~t{col 14}{c |}{res}{col 17} .4995977{col 28} .0100281{col 58}  .479943{col 70} .5192525
{txt}{hline 13}{c BT}{hline 64}
    p = proportion({res}mvcheck1b_~t{txt})                                  z = {res} 44.5421
{txt}H0: p = {res}0.166667

  {txt}Ha: p < {res}0.166667             {txt}Ha: p != {res}0.166667             {txt}Ha: p > {res}0.166667
 {txt}Pr(Z < z) = {res}1.0000         {txt}Pr(|Z| > |z|) = {res}0.0000          {txt}Pr(Z > z) = {res}0.0000
{txt}
{com}. 
. proportion mvcheck1c_Correct // proportion answering MVC correctly
{res}
{txt}{col 1}Proportion estimation{col 47}{lalign 13:Number of obs}{col 60} = {res}{ralign 5:2,470}

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 19}{c |}{col 42}             L{col 56}ogit
{col 19}{c |} Proportion{col 31}   Std. err.{col 43}     [95% con{col 56}f. interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
mvcheck1c_Correct {c |}
{space 7}Incorrect  {c |}{col 19}{res}{space 2} .2931174{col 31}{space 2}  .009159{col 42}{space 5} .2754846{col 56}{space 3} .3113937
{txt}{space 9}Correct  {c |}{col 19}{res}{space 2} .7068826{col 31}{space 2}  .009159{col 42}{space 5} .6886063{col 56}{space 3} .7245154
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. di .7068826-.16666667 // difference b/w above and answering correctly by chance
{res}.54021593
{txt}
{com}. prtest mvcheck1c_Correct==.16666667 // significance test

{txt}One-sample test of proportion                   Number of obs      = {res}     2470

{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. err.{col 44}{col 49}{col 59}[95% conf. interval]
{hline 13}{c +}{hline 64}
{col 1}mvcheck1c_~t{col 14}{c |}{res}{col 17} .7068826{col 28}  .009159{col 58} .6889314{col 70} .7248338
{txt}{hline 13}{c BT}{hline 64}
    p = proportion({res}mvcheck1c_~t{txt})                                  z = {res} 72.0414
{txt}H0: p = {res}0.166667

  {txt}Ha: p < {res}0.166667             {txt}Ha: p != {res}0.166667             {txt}Ha: p > {res}0.166667
 {txt}Pr(Z < z) = {res}1.0000         {txt}Pr(|Z| > |z|) = {res}0.0000          {txt}Pr(Z > z) = {res}0.0000
{txt}
{com}. 
. 
.         **TABLE A3:  Stadium Licenses Vignette (Lucid Results)
. ci means q125_pagesubmit // mean time spent (and CI) on MV

{txt}    Variable {c |}        Obs        Mean    Std. err.       [95% conf. interval]
{hline 13}{c +}{hline 63}
q125_pages~t {c |}{col 16}{res}     2,542{col 29} 63.30734{col 41} 8.240153{col 57} 47.14924{col 69} 79.46544{txt}

{com}. 
. proportion mvcheck2a_Correct // proportion answering MVC correctly
{res}
{txt}{col 1}Proportion estimation{col 47}{lalign 13:Number of obs}{col 60} = {res}{ralign 5:2,497}

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 19}{c |}{col 42}             L{col 56}ogit
{col 19}{c |} Proportion{col 31}   Std. err.{col 43}     [95% con{col 56}f. interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
mvcheck2a_Correct {c |}
{space 7}Incorrect  {c |}{col 19}{res}{space 2} .2595114{col 31}{space 2} .0087726{col 42}{space 5} .2426826{col 56}{space 3} .2770802
{txt}{space 9}Correct  {c |}{col 19}{res}{space 2} .7404886{col 31}{space 2} .0087726{col 42}{space 5} .7229198{col 56}{space 3} .7573174
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. di .7404886-.16666667 // difference b/w above and answering correctly by chance
{res}.57382193
{txt}
{com}. prtest mvcheck2a_Correct==.16666667 // significance test

{txt}One-sample test of proportion                   Number of obs      = {res}     2497

{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. err.{col 44}{col 49}{col 59}[95% conf. interval]
{hline 13}{c +}{hline 64}
{col 1}mvcheck2a_~t{col 14}{c |}{res}{col 17} .7404886{col 28} .0087726{col 58} .7232946{col 70} .7576826
{txt}{hline 13}{c BT}{hline 64}
    p = proportion({res}mvcheck2a_~t{txt})                                  z = {res} 76.9401
{txt}H0: p = {res}0.166667

  {txt}Ha: p < {res}0.166667             {txt}Ha: p != {res}0.166667             {txt}Ha: p > {res}0.166667
 {txt}Pr(Z < z) = {res}1.0000         {txt}Pr(|Z| > |z|) = {res}0.0000          {txt}Pr(Z > z) = {res}0.0000
{txt}
{com}. 
. proportion mvcheck2b_Correct // proportion answering MVC correctly
{res}
{txt}{col 1}Proportion estimation{col 47}{lalign 13:Number of obs}{col 60} = {res}{ralign 5:2,484}

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 19}{c |}{col 42}             L{col 56}ogit
{col 19}{c |} Proportion{col 31}   Std. err.{col 43}     [95% con{col 56}f. interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
mvcheck2b_Correct {c |}
{space 7}Incorrect  {c |}{col 19}{res}{space 2} .2105475{col 31}{space 2} .0081802{col 42}{space 5} .1949545{col 56}{space 3} .2270359
{txt}{space 9}Correct  {c |}{col 19}{res}{space 2} .7894525{col 31}{space 2} .0081802{col 42}{space 5} .7729641{col 56}{space 3} .8050455
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. di .7894525-.16666667 // difference b/w above and answering correctly by chance
{res}.62278583
{txt}
{com}. prtest mvcheck2b_Correct==.16666667 // significance test

{txt}One-sample test of proportion                   Number of obs      = {res}     2484

{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. err.{col 44}{col 49}{col 59}[95% conf. interval]
{hline 13}{c +}{hline 64}
{col 1}mvcheck2b_~t{col 14}{c |}{res}{col 17} .7894525{col 28} .0081802{col 58} .7734197{col 70} .8054853
{txt}{hline 13}{c BT}{hline 64}
    p = proportion({res}mvcheck2b_~t{txt})                                  z = {res} 83.2877
{txt}H0: p = {res}0.166667

  {txt}Ha: p < {res}0.166667             {txt}Ha: p != {res}0.166667             {txt}Ha: p > {res}0.166667
 {txt}Pr(Z < z) = {res}1.0000         {txt}Pr(|Z| > |z|) = {res}0.0000          {txt}Pr(Z > z) = {res}0.0000
{txt}
{com}. 
. proportion mvcheck2c_Correct // proportion answering MVC correctly
{res}
{txt}{col 1}Proportion estimation{col 47}{lalign 13:Number of obs}{col 60} = {res}{ralign 5:2,476}

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 19}{c |}{col 42}             L{col 56}ogit
{col 19}{c |} Proportion{col 31}   Std. err.{col 43}     [95% con{col 56}f. interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
mvcheck2c_Correct {c |}
{space 7}Incorrect  {c |}{col 19}{res}{space 2} .3804523{col 31}{space 2} .0097569{col 42}{space 5}  .361514{col 56}{space 3} .3997617
{txt}{space 9}Correct  {c |}{col 19}{res}{space 2} .6195477{col 31}{space 2} .0097569{col 42}{space 5} .6002383{col 56}{space 3}  .638486
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. di .6195477-.16666667 // difference b/w above and answering correctly by chance
{res}.45288103
{txt}
{com}. prtest mvcheck2c_Correct==.16666667 // significance test

{txt}One-sample test of proportion                   Number of obs      = {res}     2476

{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. err.{col 44}{col 49}{col 59}[95% conf. interval]
{hline 13}{c +}{hline 64}
{col 1}mvcheck2c_~t{col 14}{c |}{res}{col 17} .6195477{col 28} .0097569{col 58} .6004245{col 70} .6386708
{txt}{hline 13}{c BT}{hline 64}
    p = proportion({res}mvcheck2c_~t{txt})                                  z = {res} 60.4680
{txt}H0: p = {res}0.166667

  {txt}Ha: p < {res}0.166667             {txt}Ha: p != {res}0.166667             {txt}Ha: p > {res}0.166667
 {txt}Pr(Z < z) = {res}1.0000         {txt}Pr(|Z| > |z|) = {res}0.0000          {txt}Pr(Z > z) = {res}0.0000
{txt}
{com}. 
. 
.         *TABLE A4:  Sulfur Reductions
. ci means q133_pagesubmit // mean time spent (and CI) on MV

{txt}    Variable {c |}        Obs        Mean    Std. err.       [95% conf. interval]
{hline 13}{c +}{hline 63}
q133_pages~t {c |}{col 16}{res}     2,535{col 29} 52.82514{col 41} 2.547696{col 57} 47.82936{col 69} 57.82092{txt}

{com}. 
. proportion mvcheck3a_Correct // proportion answering MVC correctly
{res}
{txt}{col 1}Proportion estimation{col 47}{lalign 13:Number of obs}{col 60} = {res}{ralign 5:2,488}

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 19}{c |}{col 42}             L{col 56}ogit
{col 19}{c |} Proportion{col 31}   Std. err.{col 43}     [95% con{col 56}f. interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
mvcheck3a_Correct {c |}
{space 7}Incorrect  {c |}{col 19}{res}{space 2} .2001608{col 31}{space 2} .0080217{col 42}{space 5}  .184893{col 56}{space 3} .2163547
{txt}{space 9}Correct  {c |}{col 19}{res}{space 2} .7998392{col 31}{space 2} .0080217{col 42}{space 5} .7836453{col 56}{space 3}  .815107
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. di .7998392-.16666667 // difference b/w above and answering correctly by chance
{res}.63317253
{txt}
{com}. prtest mvcheck3a_Correct==.16666667 // significance test

{txt}One-sample test of proportion                   Number of obs      = {res}     2488

{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. err.{col 44}{col 49}{col 59}[95% conf. interval]
{hline 13}{c +}{hline 64}
{col 1}mvcheck3a_~t{col 14}{c |}{res}{col 17} .7998392{col 28} .0080217{col 58}  .784117{col 70} .8155614
{txt}{hline 13}{c BT}{hline 64}
    p = proportion({res}mvcheck3a_~t{txt})                                  z = {res} 84.7449
{txt}H0: p = {res}0.166667

  {txt}Ha: p < {res}0.166667             {txt}Ha: p != {res}0.166667             {txt}Ha: p > {res}0.166667
 {txt}Pr(Z < z) = {res}1.0000         {txt}Pr(|Z| > |z|) = {res}0.0000          {txt}Pr(Z > z) = {res}0.0000
{txt}
{com}. 
. proportion mvcheck3b_Correct // proportion answering MVC correctly
{res}
{txt}{col 1}Proportion estimation{col 47}{lalign 13:Number of obs}{col 60} = {res}{ralign 5:2,472}

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 19}{c |}{col 42}             L{col 56}ogit
{col 19}{c |} Proportion{col 31}   Std. err.{col 43}     [95% con{col 56}f. interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
mvcheck3b_Correct {c |}
{space 7}Incorrect  {c |}{col 19}{res}{space 2} .3891586{col 31}{space 2} .0098063{col 42}{space 5} .3701104{col 56}{space 3} .4085512
{txt}{space 9}Correct  {c |}{col 19}{res}{space 2} .6108414{col 31}{space 2} .0098063{col 42}{space 5} .5914488{col 56}{space 3} .6298896
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. di .6108414-.16666667 // difference b/w above and answering correctly by chance
{res}.44417473
{txt}
{com}. prtest mvcheck3b_Correct==.16666667 // significance test

{txt}One-sample test of proportion                   Number of obs      = {res}     2472

{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. err.{col 44}{col 49}{col 59}[95% conf. interval]
{hline 13}{c +}{hline 64}
{col 1}mvcheck3b_~t{col 14}{c |}{res}{col 17} .6108414{col 28} .0098063{col 58} .5916215{col 70} .6300613
{txt}{hline 13}{c BT}{hline 64}
    p = proportion({res}mvcheck3b_~t{txt})                                  z = {res} 59.2576
{txt}H0: p = {res}0.166667

  {txt}Ha: p < {res}0.166667             {txt}Ha: p != {res}0.166667             {txt}Ha: p > {res}0.166667
 {txt}Pr(Z < z) = {res}1.0000         {txt}Pr(|Z| > |z|) = {res}0.0000          {txt}Pr(Z > z) = {res}0.0000
{txt}
{com}. 
. proportion mvcheck3c_Correct // proportion answering MVC correctly
{res}
{txt}{col 1}Proportion estimation{col 47}{lalign 13:Number of obs}{col 60} = {res}{ralign 5:2,466}

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 19}{c |}{col 42}             L{col 56}ogit
{col 19}{c |} Proportion{col 31}   Std. err.{col 43}     [95% con{col 56}f. interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
mvcheck3c_Correct {c |}
{space 7}Incorrect  {c |}{col 19}{res}{space 2} .3329278{col 31}{space 2}   .00949{col 42}{space 5} .3145862{col 56}{space 3}   .35179
{txt}{space 9}Correct  {c |}{col 19}{res}{space 2} .6670722{col 31}{space 2}   .00949{col 42}{space 5}   .64821{col 56}{space 3} .6854138
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. di .6670722-.16666667 // difference b/w above and answering correctly by chance
{res}.50040553
{txt}
{com}. prtest mvcheck3c_Correct==.16666667 // significance test

{txt}One-sample test of proportion                   Number of obs      = {res}     2466

{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. err.{col 44}{col 49}{col 59}[95% conf. interval]
{hline 13}{c +}{hline 64}
{col 1}mvcheck3c_~t{col 14}{c |}{res}{col 17} .6670722{col 28}   .00949{col 58} .6484722{col 70} .6856722
{txt}{hline 13}{c BT}{hline 64}
    p = proportion({res}mvcheck3c_~t{txt})                                  z = {res} 66.6784
{txt}H0: p = {res}0.166667

  {txt}Ha: p < {res}0.166667             {txt}Ha: p != {res}0.166667             {txt}Ha: p > {res}0.166667
 {txt}Pr(Z < z) = {res}1.0000         {txt}Pr(|Z| > |z|) = {res}0.0000          {txt}Pr(Z > z) = {res}0.0000
{txt}
{com}. 
. 
.         *TABLE A5:  Plant Removal
. ci means q152_pagesubmit // mean time spent (and CI) on MV

{txt}    Variable {c |}        Obs        Mean    Std. err.       [95% conf. interval]
{hline 13}{c +}{hline 63}
q152_pages~t {c |}{col 16}{res}     2,540{col 29} 67.00785{col 41} 7.415838{col 57} 52.46615{col 69} 81.54956{txt}

{com}. 
. proportion mvcheck4a_Correct // proportion answering MVC correctly
{res}
{txt}{col 1}Proportion estimation{col 47}{lalign 13:Number of obs}{col 60} = {res}{ralign 5:2,512}

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 19}{c |}{col 42}             L{col 56}ogit
{col 19}{c |} Proportion{col 31}   Std. err.{col 43}     [95% con{col 56}f. interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
mvcheck4a_Correct {c |}
{space 7}Incorrect  {c |}{col 19}{res}{space 2} .1886943{col 31}{space 2} .0078066{col 42}{space 5} .1738604{col 56}{space 3} .2044806
{txt}{space 9}Correct  {c |}{col 19}{res}{space 2} .8113057{col 31}{space 2} .0078066{col 42}{space 5} .7955194{col 56}{space 3} .8261396
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. di .8113057-.16666667 // difference b/w above and answering correctly by chance
{res}.64463903
{txt}
{com}. prtest mvcheck4a_Correct==.16666667 // significance test

{txt}One-sample test of proportion                   Number of obs      = {res}     2512

{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. err.{col 44}{col 49}{col 59}[95% conf. interval]
{hline 13}{c +}{hline 64}
{col 1}mvcheck4a_~t{col 14}{c |}{res}{col 17} .8113057{col 28} .0078066{col 58} .7960051{col 70} .8266064
{txt}{hline 13}{c BT}{hline 64}
    p = proportion({res}mvcheck4a_~t{txt})                                  z = {res} 86.6947
{txt}H0: p = {res}0.166667

  {txt}Ha: p < {res}0.166667             {txt}Ha: p != {res}0.166667             {txt}Ha: p > {res}0.166667
 {txt}Pr(Z < z) = {res}1.0000         {txt}Pr(|Z| > |z|) = {res}0.0000          {txt}Pr(Z > z) = {res}0.0000
{txt}
{com}. 
. proportion mvcheck4b_Correct // proportion answering MVC correctly
{res}
{txt}{col 1}Proportion estimation{col 47}{lalign 13:Number of obs}{col 60} = {res}{ralign 5:2,497}

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 19}{c |}{col 42}             L{col 56}ogit
{col 19}{c |} Proportion{col 31}   Std. err.{col 43}     [95% con{col 56}f. interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
mvcheck4b_Correct {c |}
{space 7}Incorrect  {c |}{col 19}{res}{space 2} .4249099{col 31}{space 2} .0098925{col 42}{space 5} .4056365{col 56}{space 3} .4444143
{txt}{space 9}Correct  {c |}{col 19}{res}{space 2} .5750901{col 31}{space 2} .0098925{col 42}{space 5} .5555857{col 56}{space 3} .5943635
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. di .5750901-.16666667 // difference b/w above and answering correctly by chance
{res}.40842343
{txt}
{com}. prtest mvcheck4b_Correct==.16666667 // significance test

{txt}One-sample test of proportion                   Number of obs      = {res}     2497

{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. err.{col 44}{col 49}{col 59}[95% conf. interval]
{hline 13}{c +}{hline 64}
{col 1}mvcheck4b_~t{col 14}{c |}{res}{col 17} .5750901{col 28} .0098925{col 58} .5557011{col 70} .5944791
{txt}{hline 13}{c BT}{hline 64}
    p = proportion({res}mvcheck4b_~t{txt})                                  z = {res} 54.7629
{txt}H0: p = {res}0.166667

  {txt}Ha: p < {res}0.166667             {txt}Ha: p != {res}0.166667             {txt}Ha: p > {res}0.166667
 {txt}Pr(Z < z) = {res}1.0000         {txt}Pr(|Z| > |z|) = {res}0.0000          {txt}Pr(Z > z) = {res}0.0000
{txt}
{com}. 
. proportion mvcheck4c_Correct // proportion answering MVC correctly
{res}
{txt}{col 1}Proportion estimation{col 47}{lalign 13:Number of obs}{col 60} = {res}{ralign 5:2,493}

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 19}{c |}{col 42}             L{col 56}ogit
{col 19}{c |} Proportion{col 31}   Std. err.{col 43}     [95% con{col 56}f. interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
mvcheck4c_Correct {c |}
{space 7}Incorrect  {c |}{col 19}{res}{space 2} .4408343{col 31}{space 2} .0099437{col 42}{space 5} .4214365{col 56}{space 3} .4604145
{txt}{space 9}Correct  {c |}{col 19}{res}{space 2} .5591657{col 31}{space 2} .0099437{col 42}{space 5} .5395855{col 56}{space 3} .5785635
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. di .5591657-.16666667 // difference b/w above and answering correctly by chance
{res}.39249903
{txt}
{com}. prtest mvcheck4c_Correct==.16666667 // significance test

{txt}One-sample test of proportion                   Number of obs      = {res}     2493

{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. err.{col 44}{col 49}{col 59}[95% conf. interval]
{hline 13}{c +}{hline 64}
{col 1}mvcheck4c_~t{col 14}{c |}{res}{col 17} .5591657{col 28} .0099437{col 58} .5396764{col 70} .5786549
{txt}{hline 13}{c BT}{hline 64}
    p = proportion({res}mvcheck4c_~t{txt})                                  z = {res} 52.5855
{txt}H0: p = {res}0.166667

  {txt}Ha: p < {res}0.166667             {txt}Ha: p != {res}0.166667             {txt}Ha: p > {res}0.166667
 {txt}Pr(Z < z) = {res}1.0000         {txt}Pr(|Z| > |z|) = {res}0.0000          {txt}Pr(Z > z) = {res}0.0000
{txt}
{com}. 
. 
. ********************
. ** Table A7 Analyses
. ********************
. 
. tab MVCscale_Round2 if Treatment_Round2==0 // % answering n MVCs correctly in control groups

{txt}MVCscale_Ro {c |}
       und2 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        302       11.18       11.18
{txt}          1 {c |}{res}        419       15.51       26.69
{txt}          2 {c |}{res}        734       27.18       53.87
{txt}          3 {c |}{res}      1,246       46.13      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,701      100.00
{txt}
{com}. tab MVCscale_Round2 if Treatment_Round2==1 // % answering n MVCs correctly in treatment groups

{txt}MVCscale_Ro {c |}
       und2 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        286       10.60       10.60
{txt}          1 {c |}{res}        415       15.38       25.98
{txt}          2 {c |}{res}        728       26.98       52.97
{txt}          3 {c |}{res}      1,269       47.03      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,698      100.00
{txt}
{com}. tab MVCscale_Round2 // % answering n MVCs correctly in overall sample

{txt}MVCscale_Ro {c |}
       und2 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        597       10.97       10.97
{txt}          1 {c |}{res}        843       15.50       26.47
{txt}          2 {c |}{res}      1,471       27.04       53.51
{txt}          3 {c |}{res}      2,529       46.49      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      5,440      100.00
{txt}
{com}. 
. 
. ********************
. *Table B1. Demographic Results (restricted to those featured in model)
. ********************
. proportion mvround1 //  restrict demographics to those who were assigned in Round 1
{res}
{txt}{col 1}Proportion estimation{col 52}{lalign 13:Number of obs}{col 65} = {res}{ralign 5:5,890}

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 24}{c |}{col 47}             L{col 61}ogit
{col 24}{c |} Proportion{col 36}   Std. err.{col 48}     [95% con{col 61}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 14}mvround1 {c |}
{space 16}No MV  {c |}{col 24}{res}{space 2} .2001698{col 36}{space 2} .0052136{col 47}{space 5} .1901444{col 61}{space 3} .2105863
{txt}Scientific Publishing  {c |}{col 24}{res}{space 2}       .2{col 36}{space 2}  .005212{col 47}{space 5}  .189978{col 61}{space 3} .2104133
{txt}{space 5}Stadium Licenses  {c |}{col 24}{res}{space 2}       .2{col 36}{space 2}  .005212{col 47}{space 5}  .189978{col 61}{space 3} .2104133
{txt}{space 4}Sulfur Reductions  {c |}{col 24}{res}{space 2}       .2{col 36}{space 2}  .005212{col 47}{space 5}  .189978{col 61}{space 3} .2104133
{txt}{space 5}Hazardous Plants  {c |}{col 24}{res}{space 2} .1998302{col 36}{space 2} .0052103{col 47}{space 5} .1898116{col 61}{space 3} .2102404
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. 
. *Descriptive statistics for income, education, age, and political interest
. tabstat income educ age polint if e(sample), st(mean median)

{txt}   Stats {...}
{c |}{...}
    income      educ       age    polint
{hline 9}{c +}{hline 40}
{ralign 8:Mean} {...}
{c |}{...}
 {res} 2.789983   3.31528   48.5017  3.380136
{txt}{ralign 8:p50} {...}
{c |}{...}
 {res}        2         3        48         4
{txt}{hline 9}{c BT}{hline 40}

{com}. 
. * % female, racial groups, party and ideology
. tab1 gender race_5cat pid7 ideology  if e(sample)

{res}-> tabulation of gender if e(sample) 

     {txt}gender {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       Male {c |}{res}      3,234       54.91       54.91
{txt}     Female {c |}{res}      2,656       45.09      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      5,890      100.00

-> tabulation of race_5cat if e(sample) 

               {txt}RECODE of race {c |}      Freq.     Percent        Cum.
{hline 30}{c +}{hline 35}
           Non-Hispanic White {c |}{res}      4,399       74.69       74.69
{txt}Non-Hispanic African-American {c |}{res}        620       10.53       85.21
{txt}                     Hispanic {c |}{res}        417        7.08       92.29
{txt}                        Asian {c |}{res}        231        3.92       96.21
{txt}                        Other {c |}{res}        223        3.79      100.00
{txt}{hline 30}{c +}{hline 35}
                        Total {c |}{res}      5,890      100.00

-> tabulation of pid7 if e(sample) 

             {txt}pid7 {c |}      Freq.     Percent        Cum.
{hline 18}{c +}{hline 35}
  Strong Democrat {c |}{res}      1,027       17.44       17.44
{txt}         Democrat {c |}{res}        974       16.54       33.97
{txt}    Lean Democrat {c |}{res}        574        9.75       43.72
{txt}      Independent {c |}{res}      1,110       18.85       62.56
{txt}  Lean Republican {c |}{res}        475        8.06       70.63
{txt}       Republican {c |}{res}        906       15.38       86.01
{txt}Strong Republican {c |}{res}        824       13.99      100.00
{txt}{hline 18}{c +}{hline 35}
            Total {c |}{res}      5,890      100.00

-> tabulation of ideology if e(sample) 

              {txt}ideology {c |}      Freq.     Percent        Cum.
{hline 23}{c +}{hline 35}
     Extremely Liberal {c |}{res}        541        9.19        9.19
{txt}               Liberal {c |}{res}        822       13.96       23.14
{txt}      Slightly Liberal {c |}{res}        571        9.69       32.84
{txt}              Moderate {c |}{res}      1,908       32.39       65.23
{txt} Slightly Conservative {c |}{res}        570        9.68       74.91
{txt}          Conservative {c |}{res}        883       14.99       89.90
{txt}Extremely Conservative {c |}{res}        595       10.10      100.00
{txt}{hline 23}{c +}{hline 35}
                 Total {c |}{res}      5,890      100.00
{txt}
{com}. 
. *Additional information
. tab1 gender race_5cat educ polint pid7 ideology income if e(sample), nol

{res}-> tabulation of gender if e(sample) 

     {txt}gender {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}      3,234       54.91       54.91
{txt}          2 {c |}{res}      2,656       45.09      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      5,890      100.00

-> tabulation of race_5cat if e(sample) 

  {txt}RECODE of {c |}
       race {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}      4,399       74.69       74.69
{txt}          2 {c |}{res}        620       10.53       85.21
{txt}          3 {c |}{res}        417        7.08       92.29
{txt}          4 {c |}{res}        231        3.92       96.21
{txt}          5 {c |}{res}        223        3.79      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      5,890      100.00

-> tabulation of educ if e(sample) 

       {txt}educ {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}        132        2.24        2.24
{txt}          2 {c |}{res}      1,326       22.51       24.75
{txt}          3 {c |}{res}      1,935       32.85       57.61
{txt}          4 {c |}{res}      1,714       29.10       86.71
{txt}          5 {c |}{res}        616       10.46       97.16
{txt}          6 {c |}{res}        167        2.84      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      5,890      100.00

-> tabulation of polint if e(sample) 

     {txt}polint {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}        513        8.71        8.71
{txt}          2 {c |}{res}        986       16.74       25.45
{txt}          3 {c |}{res}      1,430       24.28       49.73
{txt}          4 {c |}{res}      1,671       28.37       78.10
{txt}          5 {c |}{res}      1,290       21.90      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      5,890      100.00

-> tabulation of pid7 if e(sample) 

       {txt}pid7 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}      1,027       17.44       17.44
{txt}          2 {c |}{res}        974       16.54       33.97
{txt}          3 {c |}{res}        574        9.75       43.72
{txt}          4 {c |}{res}      1,110       18.85       62.56
{txt}          5 {c |}{res}        475        8.06       70.63
{txt}          6 {c |}{res}        906       15.38       86.01
{txt}          7 {c |}{res}        824       13.99      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      5,890      100.00

-> tabulation of ideology if e(sample) 

   {txt}ideology {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}        541        9.19        9.19
{txt}          2 {c |}{res}        822       13.96       23.14
{txt}          3 {c |}{res}        571        9.69       32.84
{txt}          4 {c |}{res}      1,908       32.39       65.23
{txt}          5 {c |}{res}        570        9.68       74.91
{txt}          6 {c |}{res}        883       14.99       89.90
{txt}          7 {c |}{res}        595       10.10      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      5,890      100.00

-> tabulation of income if e(sample) 

     {txt}income {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}      1,386       23.53       23.53
{txt}          2 {c |}{res}      1,677       28.47       52.00
{txt}          3 {c |}{res}      1,143       19.41       71.41
{txt}          4 {c |}{res}        719       12.21       83.62
{txt}          5 {c |}{res}        580        9.85       93.46
{txt}          6 {c |}{res}        207        3.51       96.98
{txt}          7 {c |}{res}        178        3.02      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      5,890      100.00
{txt}
{com}. 
. 
. ********************
. *Table E2 Analyses
. ********************
. 
. *Better MVC Performance Predicts Greater Time Spent
. 
. **
. *Experiment 1 (Student Loan Forgiveness)
. 
. *Mock Vignette Length (200% increase)
. reg lnRound2MVtime R2MVperformance_01 i.mvround2 if expround2==1, robust // reported n size

{txt}Linear regression                               Number of obs     = {res}     1,358
                                                {txt}F(4, 1353)        =  {res}   129.75
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.3407
                                                {txt}Root MSE          =    {res} .94926

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}    lnRound2MVtime{col 20}{c |} Coefficient{col 32}  std. err.{col 44}      t{col 52}   P>|t|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
R2MVperformance_01 {c |}{col 20}{res}{space 2} 2.003937{col 32}{space 2} .0902328{col 43}{space 1}   22.21{col 52}{space 3}0.000{col 60}{space 4} 1.826926{col 73}{space 3} 2.180949
{txt}{space 18} {c |}
{space 10}mvround2 {c |}
{space 1}Stadium Licenses  {c |}{col 20}{res}{space 2}-.1569099{col 32}{space 2} .0749922{col 43}{space 1}   -2.09{col 52}{space 3}0.037{col 60}{space 4}-.3040236{col 73}{space 3}-.0097963
{txt}Sulfur Reductions  {c |}{col 20}{res}{space 2}-.2043895{col 32}{space 2} .0781141{col 43}{space 1}   -2.62{col 52}{space 3}0.009{col 60}{space 4}-.3576274{col 73}{space 3}-.0511515
{txt}{space 1}Hazardous Plants  {c |}{col 20}{res}{space 2} -.004707{col 32}{space 2} .0724994{col 43}{space 1}   -0.06{col 52}{space 3}0.948{col 60}{space 4}-.1469305{col 73}{space 3} .1375165
{txt}{space 18} {c |}
{space 13}_cons {c |}{col 20}{res}{space 2} 2.167265{col 32}{space 2} .0959093{col 43}{space 1}   22.60{col 52}{space 3}0.000{col 60}{space 4} 1.979118{col 73}{space 3} 2.355412
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Experiment Vignette Length
.                 *Control (101% increase)
. reg ln_q144_pagesubmit R2MVperformance_01 i.mvround2 if expround2==1, robust 

{txt}Linear regression                               Number of obs     = {res}       678
                                                {txt}F(4, 673)         =  {res}    27.88
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1570
                                                {txt}Root MSE          =    {res} .83147

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}ln_q144_pagesubmit{col 20}{c |} Coefficient{col 32}  std. err.{col 44}      t{col 52}   P>|t|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
R2MVperformance_01 {c |}{col 20}{res}{space 2} 1.013064{col 32}{space 2} .1006604{col 43}{space 1}   10.06{col 52}{space 3}0.000{col 60}{space 4} .8154182{col 73}{space 3} 1.210711
{txt}{space 18} {c |}
{space 10}mvround2 {c |}
{space 1}Stadium Licenses  {c |}{col 20}{res}{space 2} .0395716{col 32}{space 2} .0935355{col 43}{space 1}    0.42{col 52}{space 3}0.672{col 60}{space 4}-.1440849{col 73}{space 3} .2232281
{txt}Sulfur Reductions  {c |}{col 20}{res}{space 2} .0029071{col 32}{space 2} .0850143{col 43}{space 1}    0.03{col 52}{space 3}0.973{col 60}{space 4} -.164018{col 73}{space 3} .1698323
{txt}{space 1}Hazardous Plants  {c |}{col 20}{res}{space 2}-.0918055{col 32}{space 2} .0784791{col 43}{space 1}   -1.17{col 52}{space 3}0.242{col 60}{space 4}-.2458989{col 73}{space 3} .0622879
{txt}{space 18} {c |}
{space 13}_cons {c |}{col 20}{res}{space 2}  1.46081{col 32}{space 2} .0937867{col 43}{space 1}   15.58{col 52}{space 3}0.000{col 60}{space 4}  1.27666{col 73}{space 3}  1.64496
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
.                 *Treatment (158% increase)
. reg ln_q146_pagesubmit R2MVperformance_01 i.mvround2 if expround2==1, robust

{txt}Linear regression                               Number of obs     = {res}       678
                                                {txt}F(4, 673)         =  {res}    33.71
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2428
                                                {txt}Root MSE          =    {res} .92699

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}ln_q146_pagesubmit{col 20}{c |} Coefficient{col 32}  std. err.{col 44}      t{col 52}   P>|t|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
R2MVperformance_01 {c |}{col 20}{res}{space 2} 1.577833{col 32}{space 2} .1363769{col 43}{space 1}   11.57{col 52}{space 3}0.000{col 60}{space 4} 1.310058{col 73}{space 3} 1.845609
{txt}{space 18} {c |}
{space 10}mvround2 {c |}
{space 1}Stadium Licenses  {c |}{col 20}{res}{space 2}-.1073413{col 32}{space 2} .1079846{col 43}{space 1}   -0.99{col 52}{space 3}0.321{col 60}{space 4}-.3193686{col 73}{space 3} .1046859
{txt}Sulfur Reductions  {c |}{col 20}{res}{space 2}-.1023026{col 32}{space 2} .1037701{col 43}{space 1}   -0.99{col 52}{space 3}0.325{col 60}{space 4}-.3060548{col 73}{space 3} .1014495
{txt}{space 1}Hazardous Plants  {c |}{col 20}{res}{space 2}-.0525609{col 32}{space 2} .0903766{col 43}{space 1}   -0.58{col 52}{space 3}0.561{col 60}{space 4} -.230015{col 73}{space 3} .1248931
{txt}{space 18} {c |}
{space 13}_cons {c |}{col 20}{res}{space 2}  2.00449{col 32}{space 2} .1295942{col 43}{space 1}   15.47{col 52}{space 3}0.000{col 60}{space 4} 1.750033{col 73}{space 3} 2.258948
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Time answering outcome measure (32% increase)
. reg ln_q148_pagesubmit R2MVperformance_01 i.mvround2 if expround2==1, robust

{txt}Linear regression                               Number of obs     = {res}     1,354
                                                {txt}F(4, 1349)        =  {res}     9.25
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0324
                                                {txt}Root MSE          =    {res} .61291

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}ln_q148_pagesubmit{col 20}{c |} Coefficient{col 32}  std. err.{col 44}      t{col 52}   P>|t|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
R2MVperformance_01 {c |}{col 20}{res}{space 2}  .318917{col 32}{space 2} .0542143{col 43}{space 1}    5.88{col 52}{space 3}0.000{col 60}{space 4} .2125634{col 73}{space 3} .4252705
{txt}{space 18} {c |}
{space 10}mvround2 {c |}
{space 1}Stadium Licenses  {c |}{col 20}{res}{space 2}-.0223394{col 32}{space 2} .0489128{col 43}{space 1}   -0.46{col 52}{space 3}0.648{col 60}{space 4}-.1182929{col 73}{space 3} .0736142
{txt}Sulfur Reductions  {c |}{col 20}{res}{space 2} .0661073{col 32}{space 2} .0491237{col 43}{space 1}    1.35{col 52}{space 3}0.179{col 60}{space 4}-.0302598{col 73}{space 3} .1624744
{txt}{space 1}Hazardous Plants  {c |}{col 20}{res}{space 2} .0080521{col 32}{space 2}  .042133{col 43}{space 1}    0.19{col 52}{space 3}0.848{col 60}{space 4}-.0746012{col 73}{space 3} .0907054
{txt}{space 18} {c |}
{space 13}_cons {c |}{col 20}{res}{space 2} 1.816432{col 32}{space 2} .0522606{col 43}{space 1}   34.76{col 52}{space 3}0.000{col 60}{space 4} 1.713911{col 73}{space 3} 1.918952
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Survey duration (63% increase)
. reg ln_durationinseconds R2MVperformance_01 i.mvround2 if expround2==1, robust 

{txt}Linear regression                               Number of obs     = {res}     1,358
                                                {txt}F(4, 1353)        =  {res}    27.23
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0905
                                                {txt}Root MSE          =    {res} .68538

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}ln_durationinsec~s{col 20}{c |} Coefficient{col 32}  std. err.{col 44}      t{col 52}   P>|t|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
R2MVperformance_01 {c |}{col 20}{res}{space 2} .6329185{col 32}{space 2} .0606602{col 43}{space 1}   10.43{col 52}{space 3}0.000{col 60}{space 4} .5139201{col 73}{space 3} .7519168
{txt}{space 18} {c |}
{space 10}mvround2 {c |}
{space 1}Stadium Licenses  {c |}{col 20}{res}{space 2}-.0276666{col 32}{space 2} .0496147{col 43}{space 1}   -0.56{col 52}{space 3}0.577{col 60}{space 4}-.1249968{col 73}{space 3} .0696635
{txt}Sulfur Reductions  {c |}{col 20}{res}{space 2} .0612331{col 32}{space 2} .0570386{col 43}{space 1}    1.07{col 52}{space 3}0.283{col 60}{space 4}-.0506606{col 73}{space 3} .1731269
{txt}{space 1}Hazardous Plants  {c |}{col 20}{res}{space 2} .0111448{col 32}{space 2}  .049168{col 43}{space 1}    0.23{col 52}{space 3}0.821{col 60}{space 4}-.0853091{col 73}{space 3} .1075987
{txt}{space 18} {c |}
{space 13}_cons {c |}{col 20}{res}{space 2} 6.466779{col 32}{space 2} .0602723{col 43}{space 1}  107.29{col 52}{space 3}0.000{col 60}{space 4} 6.348542{col 73}{space 3} 6.585016
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Answering FMC correctly
. logit SLexpFMC_Correct R2MVperformance_01 i.mvround2 if expround2==1

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-940.91681}  
Iteration 1:{space 3}log likelihood = {res:-882.70359}  
Iteration 2:{space 3}log likelihood = {res:-882.57889}  
Iteration 3:{space 3}log likelihood = {res:-882.57889}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,358}
{txt}{col 57}{lalign 13:LR chi2({res:4})}{col 70} = {res}{ralign 6:116.68}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-882.57889}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0620}

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  SLexpFMC_Correct{col 20}{c |} Coefficient{col 32}  Std. err.{col 44}      z{col 52}   P>|z|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
R2MVperformance_01 {c |}{col 20}{res}{space 2} 1.755572{col 32}{space 2} .1799595{col 43}{space 1}    9.76{col 52}{space 3}0.000{col 60}{space 4} 1.402858{col 73}{space 3} 2.108286
{txt}{space 18} {c |}
{space 10}mvround2 {c |}
{space 1}Stadium Licenses  {c |}{col 20}{res}{space 2}-.3253275{col 32}{space 2} .1630658{col 43}{space 1}   -2.00{col 52}{space 3}0.046{col 60}{space 4}-.6449307{col 73}{space 3}-.0057243
{txt}Sulfur Reductions  {c |}{col 20}{res}{space 2}-.5447279{col 32}{space 2} .1627011{col 43}{space 1}   -3.35{col 52}{space 3}0.001{col 60}{space 4}-.8636162{col 73}{space 3}-.2258395
{txt}{space 1}Hazardous Plants  {c |}{col 20}{res}{space 2}-.1661291{col 32}{space 2} .1566556{col 43}{space 1}   -1.06{col 52}{space 3}0.289{col 60}{space 4}-.4731685{col 73}{space 3} .1409102
{txt}{space 18} {c |}
{space 13}_cons {c |}{col 20}{res}{space 2}-1.043829{col 32}{space 2} .1710171{col 43}{space 1}   -6.10{col 52}{space 3}0.000{col 60}{space 4}-1.379016{col 73}{space 3}-.7086416
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, dydx(R2MVperformance_01) // effect = .40
{res}
{txt}{col 1}Average marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:1,358}
{txt}{col 1}Model VCE: {res:OIM}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Pr(SLexpFMC_Correct), predict()}{p_end}
{p2col:dy/dx wrt:}{res:R2MVperformance_01}{p_end}
{p2colreset}{...}

{res}{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32} Delta-method
{col 20}{c |}      dy/dx{col 32}   std. err.{col 44}      z{col 52}   P>|z|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
R2MVperformance_01 {c |}{col 20}{res}{space 2}  .402396{col 32}{space 2} .0354407{col 43}{space 1}   11.35{col 52}{space 3}0.000{col 60}{space 4} .3329334{col 73}{space 3} .4718585
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. *******************************
.         *Experiment 2 (KKK) 
. *******************************
. 
. *Mock Vignette length (172% increase)
. reg lnRound2MVtime R2MVperformance_01 i.mvround2 if expround2==2, robust // reported n size

{txt}Linear regression                               Number of obs     = {res}     1,362
                                                {txt}F(4, 1357)        =  {res}    79.09
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2648
                                                {txt}Root MSE          =    {res} .98564

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}    lnRound2MVtime{col 20}{c |} Coefficient{col 32}  std. err.{col 44}      t{col 52}   P>|t|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
R2MVperformance_01 {c |}{col 20}{res}{space 2} 1.723688{col 32}{space 2} .0980298{col 43}{space 1}   17.58{col 52}{space 3}0.000{col 60}{space 4} 1.531382{col 73}{space 3} 1.915995
{txt}{space 18} {c |}
{space 10}mvround2 {c |}
{space 1}Stadium Licenses  {c |}{col 20}{res}{space 2} .0664342{col 32}{space 2} .0725627{col 43}{space 1}    0.92{col 52}{space 3}0.360{col 60}{space 4}-.0759131{col 73}{space 3} .2087814
{txt}Sulfur Reductions  {c |}{col 20}{res}{space 2}-.0279384{col 32}{space 2} .0776481{col 43}{space 1}   -0.36{col 52}{space 3}0.719{col 60}{space 4}-.1802618{col 73}{space 3}  .124385
{txt}{space 1}Hazardous Plants  {c |}{col 20}{res}{space 2} .1826109{col 32}{space 2} .0736916{col 43}{space 1}    2.48{col 52}{space 3}0.013{col 60}{space 4}  .038049{col 73}{space 3} .3271728
{txt}{space 18} {c |}
{space 13}_cons {c |}{col 20}{res}{space 2}  2.26351{col 32}{space 2} .0931311{col 43}{space 1}   24.30{col 52}{space 3}0.000{col 60}{space 4} 2.080814{col 73}{space 3} 2.446207
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Experiment vignette length
.                 *Public order (control) (170% increase)
. reg ln_q184_pagesubmit R2MVperformance_01 i.mvround2 if expround2==2, robust    

{txt}Linear regression                               Number of obs     = {res}       670
                                                {txt}F(4, 665)         =  {res}    45.38
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2477
                                                {txt}Root MSE          =    {res} 1.0211

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}ln_q184_pagesubmit{col 20}{c |} Coefficient{col 32}  std. err.{col 44}      t{col 52}   P>|t|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
R2MVperformance_01 {c |}{col 20}{res}{space 2} 1.700425{col 32}{space 2} .1263544{col 43}{space 1}   13.46{col 52}{space 3}0.000{col 60}{space 4} 1.452323{col 73}{space 3} 1.948527
{txt}{space 18} {c |}
{space 10}mvround2 {c |}
{space 1}Stadium Licenses  {c |}{col 20}{res}{space 2}-.3226525{col 32}{space 2} .1123845{col 43}{space 1}   -2.87{col 52}{space 3}0.004{col 60}{space 4}-.5433237{col 73}{space 3}-.1019813
{txt}Sulfur Reductions  {c |}{col 20}{res}{space 2}-.2909579{col 32}{space 2} .1194438{col 43}{space 1}   -2.44{col 52}{space 3}0.015{col 60}{space 4}-.5254904{col 73}{space 3}-.0564254
{txt}{space 1}Hazardous Plants  {c |}{col 20}{res}{space 2}-.2098818{col 32}{space 2} .1143394{col 43}{space 1}   -1.84{col 52}{space 3}0.067{col 60}{space 4}-.4343915{col 73}{space 3}  .014628
{txt}{space 18} {c |}
{space 13}_cons {c |}{col 20}{res}{space 2} 2.848087{col 32}{space 2}  .122486{col 43}{space 1}   23.25{col 52}{space 3}0.000{col 60}{space 4} 2.607581{col 73}{space 3} 3.088593
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
.         *Free speech (treatment) (162% increase)
. reg ln_q182_pagesubmit R2MVperformance_01 i.mvround2 if expround2==2, robust

{txt}Linear regression                               Number of obs     = {res}       674
                                                {txt}F(4, 669)         =  {res}    36.90
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2497
                                                {txt}Root MSE          =    {res} .95249

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}ln_q182_pagesubmit{col 20}{c |} Coefficient{col 32}  std. err.{col 44}      t{col 52}   P>|t|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
R2MVperformance_01 {c |}{col 20}{res}{space 2} 1.618182{col 32}{space 2} .1363071{col 43}{space 1}   11.87{col 52}{space 3}0.000{col 60}{space 4} 1.350541{col 73}{space 3} 1.885823
{txt}{space 18} {c |}
{space 10}mvround2 {c |}
{space 1}Stadium Licenses  {c |}{col 20}{res}{space 2}-.1306632{col 32}{space 2} .1041527{col 43}{space 1}   -1.25{col 52}{space 3}0.210{col 60}{space 4}-.3351687{col 73}{space 3} .0738423
{txt}Sulfur Reductions  {c |}{col 20}{res}{space 2} .1091297{col 32}{space 2} .1028887{col 43}{space 1}    1.06{col 52}{space 3}0.289{col 60}{space 4}-.0928941{col 73}{space 3} .3111534
{txt}{space 1}Hazardous Plants  {c |}{col 20}{res}{space 2} .0486613{col 32}{space 2} .0974072{col 43}{space 1}    0.50{col 52}{space 3}0.618{col 60}{space 4}-.1425993{col 73}{space 3} .2399219
{txt}{space 18} {c |}
{space 13}_cons {c |}{col 20}{res}{space 2} 2.674825{col 32}{space 2} .1251067{col 43}{space 1}   21.38{col 52}{space 3}0.000{col 60}{space 4} 2.429176{col 73}{space 3} 2.920474
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Time answering outcome measure (34% increase)
. reg ln_q186_pagesubmit R2MVperformance_01 i.mvround2 if expround2==2, robust // significant

{txt}Linear regression                               Number of obs     = {res}     1,344
                                                {txt}F(4, 1339)        =  {res}     8.66
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0325
                                                {txt}Root MSE          =    {res} .64393

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}ln_q186_pagesubmit{col 20}{c |} Coefficient{col 32}  std. err.{col 44}      t{col 52}   P>|t|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
R2MVperformance_01 {c |}{col 20}{res}{space 2} .3352305{col 32}{space 2}  .061741{col 43}{space 1}    5.43{col 52}{space 3}0.000{col 60}{space 4} .2141108{col 73}{space 3} .4563502
{txt}{space 18} {c |}
{space 10}mvround2 {c |}
{space 1}Stadium Licenses  {c |}{col 20}{res}{space 2}-.0828754{col 32}{space 2}  .048199{col 43}{space 1}   -1.72{col 52}{space 3}0.086{col 60}{space 4}-.1774291{col 73}{space 3} .0116782
{txt}Sulfur Reductions  {c |}{col 20}{res}{space 2} .0281362{col 32}{space 2} .0524221{col 43}{space 1}    0.54{col 52}{space 3}0.592{col 60}{space 4}-.0747022{col 73}{space 3} .1309746
{txt}{space 1}Hazardous Plants  {c |}{col 20}{res}{space 2} .0077941{col 32}{space 2} .0496461{col 43}{space 1}    0.16{col 52}{space 3}0.875{col 60}{space 4}-.0895985{col 73}{space 3} .1051867
{txt}{space 18} {c |}
{space 13}_cons {c |}{col 20}{res}{space 2} 1.988568{col 32}{space 2} .0588757{col 43}{space 1}   33.78{col 52}{space 3}0.000{col 60}{space 4} 1.873069{col 73}{space 3} 2.104066
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Survey duration (64% increase)
. reg ln_durationinseconds R2MVperformance_01 i.mvround2 if expround2==2, robust 

{txt}Linear regression                               Number of obs     = {res}     1,362
                                                {txt}F(4, 1357)        =  {res}    27.35
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0833
                                                {txt}Root MSE          =    {res} .73345

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}ln_durationinsec~s{col 20}{c |} Coefficient{col 32}  std. err.{col 44}      t{col 52}   P>|t|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
R2MVperformance_01 {c |}{col 20}{res}{space 2} .6431196{col 32}{space 2} .0637487{col 43}{space 1}   10.09{col 52}{space 3}0.000{col 60}{space 4} .5180628{col 73}{space 3} .7681764
{txt}{space 18} {c |}
{space 10}mvround2 {c |}
{space 1}Stadium Licenses  {c |}{col 20}{res}{space 2}-.0886593{col 32}{space 2} .0514837{col 43}{space 1}   -1.72{col 52}{space 3}0.085{col 60}{space 4}-.1896557{col 73}{space 3}  .012337
{txt}Sulfur Reductions  {c |}{col 20}{res}{space 2} .0056855{col 32}{space 2} .0567946{col 43}{space 1}    0.10{col 52}{space 3}0.920{col 60}{space 4}-.1057293{col 73}{space 3} .1171002
{txt}{space 1}Hazardous Plants  {c |}{col 20}{res}{space 2}  .080118{col 32}{space 2} .0567187{col 43}{space 1}    1.41{col 52}{space 3}0.158{col 60}{space 4}-.0311478{col 73}{space 3} .1913838
{txt}{space 18} {c |}
{space 13}_cons {c |}{col 20}{res}{space 2} 6.533491{col 32}{space 2} .0572172{col 43}{space 1}  114.19{col 52}{space 3}0.000{col 60}{space 4} 6.421247{col 73}{space 3} 6.645734
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Answering FMC correctly
. logit KKKexpFMC_Correct R2MVperformance_01 i.mvround2 if expround2==2

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-789.91652}  
Iteration 1:{space 3}log likelihood = {res:-644.00982}  
Iteration 2:{space 3}log likelihood = {res:-639.43259}  
Iteration 3:{space 3}log likelihood = {res:-639.41313}  
Iteration 4:{space 3}log likelihood = {res:-639.41313}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,344}
{txt}{col 57}{lalign 13:LR chi2({res:4})}{col 70} = {res}{ralign 6:301.01}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-639.41313}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.1905}

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1} KKKexpFMC_Correct{col 20}{c |} Coefficient{col 32}  Std. err.{col 44}      z{col 52}   P>|z|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
R2MVperformance_01 {c |}{col 20}{res}{space 2} 3.262515{col 32}{space 2} .2099797{col 43}{space 1}   15.54{col 52}{space 3}0.000{col 60}{space 4} 2.850962{col 73}{space 3} 3.674068
{txt}{space 18} {c |}
{space 10}mvround2 {c |}
{space 1}Stadium Licenses  {c |}{col 20}{res}{space 2}-.0419999{col 32}{space 2}  .203793{col 43}{space 1}   -0.21{col 52}{space 3}0.837{col 60}{space 4}-.4414268{col 73}{space 3} .3574269
{txt}Sulfur Reductions  {c |}{col 20}{res}{space 2} -.256432{col 32}{space 2} .1953376{col 43}{space 1}   -1.31{col 52}{space 3}0.189{col 60}{space 4}-.6392867{col 73}{space 3} .1264227
{txt}{space 1}Hazardous Plants  {c |}{col 20}{res}{space 2}  .266897{col 32}{space 2} .1960943{col 43}{space 1}    1.36{col 52}{space 3}0.173{col 60}{space 4}-.1174408{col 73}{space 3} .6512348
{txt}{space 18} {c |}
{space 13}_cons {c |}{col 20}{res}{space 2}-1.109247{col 32}{space 2} .1852722{col 43}{space 1}   -5.99{col 52}{space 3}0.000{col 60}{space 4}-1.472374{col 73}{space 3}-.7461207
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, dydx(R2MVperformance_01) // effect=.50
{res}
{txt}{col 1}Average marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:1,344}
{txt}{col 1}Model VCE: {res:OIM}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Pr(KKKexpFMC_Correct), predict()}{p_end}
{p2col:dy/dx wrt:}{res:R2MVperformance_01}{p_end}
{p2colreset}{...}

{res}{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32} Delta-method
{col 20}{c |}      dy/dx{col 32}   std. err.{col 44}      z{col 52}   P>|z|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
R2MVperformance_01 {c |}{col 20}{res}{space 2} .4998334{col 32}{space 2} .0206461{col 43}{space 1}   24.21{col 52}{space 3}0.000{col 60}{space 4} .4593677{col 73}{space 3} .5402991
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. *******************************
.         *Experiment 3 (Deservingness) 
. *******************************
. 
. *Mock Vignette length (203% increase)
. reg lnRound2MVtime R2MVperformance_01 i.mvround2 if expround2==3, robust // reported n size

{txt}Linear regression                               Number of obs     = {res}     1,359
                                                {txt}F(4, 1354)        =  {res}   121.60
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.3318
                                                {txt}Root MSE          =    {res} 1.0015

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}    lnRound2MVtime{col 20}{c |} Coefficient{col 32}  std. err.{col 44}      t{col 52}   P>|t|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
R2MVperformance_01 {c |}{col 20}{res}{space 2} 2.026065{col 32}{space 2} .0937399{col 43}{space 1}   21.61{col 52}{space 3}0.000{col 60}{space 4} 1.842174{col 73}{space 3} 2.209956
{txt}{space 18} {c |}
{space 10}mvround2 {c |}
{space 1}Stadium Licenses  {c |}{col 20}{res}{space 2} .0577135{col 32}{space 2} .0765402{col 43}{space 1}    0.75{col 52}{space 3}0.451{col 60}{space 4}-.0924367{col 73}{space 3} .2078637
{txt}Sulfur Reductions  {c |}{col 20}{res}{space 2}-.0107048{col 32}{space 2} .0775822{col 43}{space 1}   -0.14{col 52}{space 3}0.890{col 60}{space 4}-.1628992{col 73}{space 3} .1414896
{txt}{space 1}Hazardous Plants  {c |}{col 20}{res}{space 2} .1973905{col 32}{space 2} .0828748{col 43}{space 1}    2.38{col 52}{space 3}0.017{col 60}{space 4} .0348136{col 73}{space 3} .3599674
{txt}{space 18} {c |}
{space 13}_cons {c |}{col 20}{res}{space 2} 2.000253{col 32}{space 2} .0953164{col 43}{space 1}   20.99{col 52}{space 3}0.000{col 60}{space 4}  1.81327{col 73}{space 3} 2.187237
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Experiment vignette length
.                 *unlucky (control) (157% increase)
. reg ln_q54_pagesubmit R2MVperformance_01 i.mvround2 if expround2==3, robust     

{txt}Linear regression                               Number of obs     = {res}       674
                                                {txt}F(4, 669)         =  {res}    34.91
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2369
                                                {txt}Root MSE          =    {res} .98404

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1} ln_q54_pagesubmit{col 20}{c |} Coefficient{col 32}  std. err.{col 44}      t{col 52}   P>|t|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
R2MVperformance_01 {c |}{col 20}{res}{space 2} 1.572724{col 32}{space 2} .1355878{col 43}{space 1}   11.60{col 52}{space 3}0.000{col 60}{space 4} 1.306495{col 73}{space 3} 1.838953
{txt}{space 18} {c |}
{space 10}mvround2 {c |}
{space 1}Stadium Licenses  {c |}{col 20}{res}{space 2} .0423854{col 32}{space 2} .1034691{col 43}{space 1}    0.41{col 52}{space 3}0.682{col 60}{space 4}-.1607779{col 73}{space 3} .2455486
{txt}Sulfur Reductions  {c |}{col 20}{res}{space 2}-.0800102{col 32}{space 2} .1042885{col 43}{space 1}   -0.77{col 52}{space 3}0.443{col 60}{space 4}-.2847824{col 73}{space 3} .1247621
{txt}{space 1}Hazardous Plants  {c |}{col 20}{res}{space 2} .1390889{col 32}{space 2} .1159761{col 43}{space 1}    1.20{col 52}{space 3}0.231{col 60}{space 4}-.0886321{col 73}{space 3} .3668099
{txt}{space 18} {c |}
{space 13}_cons {c |}{col 20}{res}{space 2} 2.033519{col 32}{space 2} .1392782{col 43}{space 1}   14.60{col 52}{space 3}0.000{col 60}{space 4} 1.760044{col 73}{space 3} 2.306994
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         
.                 *lazy (treatment) (155% increase)
. reg ln_q12_pagesubmit R2MVperformance_01 i.mvround2 if expround2==3, robust

{txt}Linear regression                               Number of obs     = {res}       675
                                                {txt}F(4, 670)         =  {res}    36.84
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2273
                                                {txt}Root MSE          =    {res} 1.0026

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1} ln_q12_pagesubmit{col 20}{c |} Coefficient{col 32}  std. err.{col 44}      t{col 52}   P>|t|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
R2MVperformance_01 {c |}{col 20}{res}{space 2}  1.55217{col 32}{space 2} .1319572{col 43}{space 1}   11.76{col 52}{space 3}0.000{col 60}{space 4} 1.293071{col 73}{space 3}  1.81127
{txt}{space 18} {c |}
{space 10}mvround2 {c |}
{space 1}Stadium Licenses  {c |}{col 20}{res}{space 2}-.0682076{col 32}{space 2} .1152999{col 43}{space 1}   -0.59{col 52}{space 3}0.554{col 60}{space 4}-.2946001{col 73}{space 3}  .158185
{txt}Sulfur Reductions  {c |}{col 20}{res}{space 2}-.2379847{col 32}{space 2} .1142442{col 43}{space 1}   -2.08{col 52}{space 3}0.038{col 60}{space 4}-.4623044{col 73}{space 3}-.0136649
{txt}{space 1}Hazardous Plants  {c |}{col 20}{res}{space 2} -.047419{col 32}{space 2} .1158244{col 43}{space 1}   -0.41{col 52}{space 3}0.682{col 60}{space 4}-.2748416{col 73}{space 3} .1800036
{txt}{space 18} {c |}
{space 13}_cons {c |}{col 20}{res}{space 2} 2.027323{col 32}{space 2} .1445103{col 43}{space 1}   14.03{col 52}{space 3}0.000{col 60}{space 4} 1.743575{col 73}{space 3}  2.31107
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Time answering outcome measure (67% increase)
. reg ln_q11_pagesubmit R2MVperformance_01 i.mvround2 if expround2==3, robust

{txt}Linear regression                               Number of obs     = {res}     1,348
                                                {txt}F(4, 1343)        =  {res}    26.14
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0983
                                                {txt}Root MSE          =    {res} .70095

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1} ln_q11_pagesubmit{col 20}{c |} Coefficient{col 32}  std. err.{col 44}      t{col 52}   P>|t|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
R2MVperformance_01 {c |}{col 20}{res}{space 2} .6675561{col 32}{space 2} .0665682{col 43}{space 1}   10.03{col 52}{space 3}0.000{col 60}{space 4} .5369672{col 73}{space 3} .7981451
{txt}{space 18} {c |}
{space 10}mvround2 {c |}
{space 1}Stadium Licenses  {c |}{col 20}{res}{space 2}-.0220497{col 32}{space 2} .0551592{col 43}{space 1}   -0.40{col 52}{space 3}0.689{col 60}{space 4}-.1302572{col 73}{space 3} .0861579
{txt}Sulfur Reductions  {c |}{col 20}{res}{space 2}-.0165024{col 32}{space 2} .0585699{col 43}{space 1}   -0.28{col 52}{space 3}0.778{col 60}{space 4}-.1314008{col 73}{space 3}  .098396
{txt}{space 1}Hazardous Plants  {c |}{col 20}{res}{space 2}-.0147353{col 32}{space 2} .0561671{col 43}{space 1}   -0.26{col 52}{space 3}0.793{col 60}{space 4}-.1249201{col 73}{space 3} .0954494
{txt}{space 18} {c |}
{space 13}_cons {c |}{col 20}{res}{space 2}  2.09674{col 32}{space 2} .0716493{col 43}{space 1}   29.26{col 52}{space 3}0.000{col 60}{space 4} 1.956183{col 73}{space 3} 2.237297
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Survey duration (65% increase)
. reg ln_durationinseconds R2MVperformance_01 i.mvround2 if expround2==3, robust

{txt}Linear regression                               Number of obs     = {res}     1,359
                                                {txt}F(4, 1354)        =  {res}    26.90
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0788
                                                {txt}Root MSE          =    {res} .77822

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}ln_durationinsec~s{col 20}{c |} Coefficient{col 32}  std. err.{col 44}      t{col 52}   P>|t|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
R2MVperformance_01 {c |}{col 20}{res}{space 2} .6541283{col 32}{space 2} .0637368{col 43}{space 1}   10.26{col 52}{space 3}0.000{col 60}{space 4} .5290946{col 73}{space 3}  .779162
{txt}{space 18} {c |}
{space 10}mvround2 {c |}
{space 1}Stadium Licenses  {c |}{col 20}{res}{space 2}-.0407635{col 32}{space 2} .0606931{col 43}{space 1}   -0.67{col 52}{space 3}0.502{col 60}{space 4}-.1598262{col 73}{space 3} .0782992
{txt}Sulfur Reductions  {c |}{col 20}{res}{space 2}-.0929485{col 32}{space 2} .0595495{col 43}{space 1}   -1.56{col 52}{space 3}0.119{col 60}{space 4}-.2097678{col 73}{space 3} .0238709
{txt}{space 1}Hazardous Plants  {c |}{col 20}{res}{space 2}-.0090309{col 32}{space 2} .0600225{col 43}{space 1}   -0.15{col 52}{space 3}0.880{col 60}{space 4}-.1267781{col 73}{space 3} .1087163
{txt}{space 18} {c |}
{space 13}_cons {c |}{col 20}{res}{space 2} 6.539592{col 32}{space 2} .0629408{col 43}{space 1}  103.90{col 52}{space 3}0.000{col 60}{space 4}  6.41612{col 73}{space 3} 6.663064
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Answering FMC correctly
. logit APfmc_Correct R2MVperformance_01 i.mvround2 if expround2==3

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-678.32244}  
Iteration 1:{space 3}log likelihood = {res:-537.46595}  
Iteration 2:{space 3}log likelihood = {res:  -524.313}  
Iteration 3:{space 3}log likelihood = {res:-524.18041}  
Iteration 4:{space 3}log likelihood = {res:-524.18033}  
Iteration 5:{space 3}log likelihood = {res:-524.18033}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,344}
{txt}{col 57}{lalign 13:LR chi2({res:4})}{col 70} = {res}{ralign 6:308.28}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-524.18033}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.2272}

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     APfmc_Correct{col 20}{c |} Coefficient{col 32}  Std. err.{col 44}      z{col 52}   P>|z|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
R2MVperformance_01 {c |}{col 20}{res}{space 2} 3.545736{col 32}{space 2} .2274845{col 43}{space 1}   15.59{col 52}{space 3}0.000{col 60}{space 4} 3.099874{col 73}{space 3} 3.991597
{txt}{space 18} {c |}
{space 10}mvround2 {c |}
{space 1}Stadium Licenses  {c |}{col 20}{res}{space 2}-.0504612{col 32}{space 2}  .219055{col 43}{space 1}   -0.23{col 52}{space 3}0.818{col 60}{space 4}-.4798011{col 73}{space 3} .3788787
{txt}Sulfur Reductions  {c |}{col 20}{res}{space 2}-.2270336{col 32}{space 2} .2158261{col 43}{space 1}   -1.05{col 52}{space 3}0.293{col 60}{space 4} -.650045{col 73}{space 3} .1959777
{txt}{space 1}Hazardous Plants  {c |}{col 20}{res}{space 2} .1583974{col 32}{space 2} .2244468{col 43}{space 1}    0.71{col 52}{space 3}0.480{col 60}{space 4}-.2815103{col 73}{space 3} .5983051
{txt}{space 18} {c |}
{space 13}_cons {c |}{col 20}{res}{space 2}-.6701462{col 32}{space 2} .1855806{col 43}{space 1}   -3.61{col 52}{space 3}0.000{col 60}{space 4}-1.033878{col 73}{space 3}-.3064148
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, dydx(R2MVperformance_01) // effect = .43
{res}
{txt}{col 1}Average marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:1,344}
{txt}{col 1}Model VCE: {res:OIM}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Pr(APfmc_Correct), predict()}{p_end}
{p2col:dy/dx wrt:}{res:R2MVperformance_01}{p_end}
{p2colreset}{...}

{res}{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32} Delta-method
{col 20}{c |}      dy/dx{col 32}   std. err.{col 44}      z{col 52}   P>|z|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
R2MVperformance_01 {c |}{col 20}{res}{space 2} .4302181{col 32}{space 2}  .018273{col 43}{space 1}   23.54{col 52}{space 3}0.000{col 60}{space 4} .3944038{col 73}{space 3} .4660325
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. *******************************
.         *Experiment 4 (Immigration) 
. *******************************
. 
. *Mock Vignette length (205% increase)
. reg lnRound2MVtime R2MVperformance_01 i.mvround2 if expround2==4, robust // reported n size

{txt}Linear regression                               Number of obs     = {res}     1,356
                                                {txt}F(4, 1351)        =  {res}   143.68
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.3559
                                                {txt}Root MSE          =    {res} .94916

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}    lnRound2MVtime{col 20}{c |} Coefficient{col 32}  std. err.{col 44}      t{col 52}   P>|t|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
R2MVperformance_01 {c |}{col 20}{res}{space 2} 2.054851{col 32}{space 2} .0865038{col 43}{space 1}   23.75{col 52}{space 3}0.000{col 60}{space 4} 1.885155{col 73}{space 3} 2.224547
{txt}{space 18} {c |}
{space 10}mvround2 {c |}
{space 1}Stadium Licenses  {c |}{col 20}{res}{space 2}-.1637995{col 32}{space 2} .0745331{col 43}{space 1}   -2.20{col 52}{space 3}0.028{col 60}{space 4}-.3100128{col 73}{space 3}-.0175863
{txt}Sulfur Reductions  {c |}{col 20}{res}{space 2}-.2202401{col 32}{space 2} .0715594{col 43}{space 1}   -3.08{col 52}{space 3}0.002{col 60}{space 4}-.3606198{col 73}{space 3}-.0798605
{txt}{space 1}Hazardous Plants  {c |}{col 20}{res}{space 2} .0827412{col 32}{space 2} .0738388{col 43}{space 1}    1.12{col 52}{space 3}0.263{col 60}{space 4}-.0621099{col 73}{space 3} .2275923
{txt}{space 18} {c |}
{space 13}_cons {c |}{col 20}{res}{space 2} 2.115129{col 32}{space 2} .0872289{col 43}{space 1}   24.25{col 52}{space 3}0.000{col 60}{space 4}  1.94401{col 73}{space 3} 2.286248
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Experiment vignette length
.                 *Control (Low status middle-eastern) (168% increase)
. reg ln_q160_pagesubmit R2MVperformance_01 i.mvround2 if expround2==4, robust

{txt}Linear regression                               Number of obs     = {res}       675
                                                {txt}F(4, 670)         =  {res}    47.34
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2903
                                                {txt}Root MSE          =    {res} .90933

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}ln_q160_pagesubmit{col 20}{c |} Coefficient{col 32}  std. err.{col 44}      t{col 52}   P>|t|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
R2MVperformance_01 {c |}{col 20}{res}{space 2} 1.683845{col 32}{space 2} .1241408{col 43}{space 1}   13.56{col 52}{space 3}0.000{col 60}{space 4} 1.440093{col 73}{space 3} 1.927596
{txt}{space 18} {c |}
{space 10}mvround2 {c |}
{space 1}Stadium Licenses  {c |}{col 20}{res}{space 2}-.2303932{col 32}{space 2} .0975985{col 43}{space 1}   -2.36{col 52}{space 3}0.019{col 60}{space 4}-.4220289{col 73}{space 3}-.0387574
{txt}Sulfur Reductions  {c |}{col 20}{res}{space 2}-.2563543{col 32}{space 2} .0903914{col 43}{space 1}   -2.84{col 52}{space 3}0.005{col 60}{space 4}-.4338389{col 73}{space 3}-.0788697
{txt}{space 1}Hazardous Plants  {c |}{col 20}{res}{space 2} .0417515{col 32}{space 2} .0978226{col 43}{space 1}    0.43{col 52}{space 3}0.670{col 60}{space 4}-.1503242{col 73}{space 3} .2338272
{txt}{space 18} {c |}
{space 13}_cons {c |}{col 20}{res}{space 2} 2.176306{col 32}{space 2} .1118111{col 43}{space 1}   19.46{col 52}{space 3}0.000{col 60}{space 4} 1.956763{col 73}{space 3} 2.395848
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
.                 *Treatment (high stuatus mexican) (164% increase)
. reg ln_q162_pagesubmit R2MVperformance_01 i.mvround2 if expround2==4, robust

{txt}Linear regression                               Number of obs     = {res}       675
                                                {txt}F(4, 670)         =  {res}    37.74
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2618
                                                {txt}Root MSE          =    {res} .93938

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}ln_q162_pagesubmit{col 20}{c |} Coefficient{col 32}  std. err.{col 44}      t{col 52}   P>|t|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
R2MVperformance_01 {c |}{col 20}{res}{space 2} 1.638821{col 32}{space 2} .1345842{col 43}{space 1}   12.18{col 52}{space 3}0.000{col 60}{space 4} 1.374564{col 73}{space 3} 1.903079
{txt}{space 18} {c |}
{space 10}mvround2 {c |}
{space 1}Stadium Licenses  {c |}{col 20}{res}{space 2}-.1064215{col 32}{space 2} .1015484{col 43}{space 1}   -1.05{col 52}{space 3}0.295{col 60}{space 4}-.3058128{col 73}{space 3} .0929699
{txt}Sulfur Reductions  {c |}{col 20}{res}{space 2}-.0955762{col 32}{space 2} .1009303{col 43}{space 1}   -0.95{col 52}{space 3}0.344{col 60}{space 4} -.293754{col 73}{space 3} .1026017
{txt}{space 1}Hazardous Plants  {c |}{col 20}{res}{space 2} .0519681{col 32}{space 2} .0997828{col 43}{space 1}    0.52{col 52}{space 3}0.603{col 60}{space 4}-.1439565{col 73}{space 3} .2478927
{txt}{space 18} {c |}
{space 13}_cons {c |}{col 20}{res}{space 2} 2.114225{col 32}{space 2} .1281041{col 43}{space 1}   16.50{col 52}{space 3}0.000{col 60}{space 4} 1.862691{col 73}{space 3} 2.365759
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Time answering outcome measures (combined all three) (63% increase)
. reg ln_exp4_totaltime R2MVperformance_01 i.mvround2 if expround2==4, robust

{txt}Linear regression                               Number of obs     = {res}     1,348
                                                {txt}F(4, 1343)        =  {res}    27.81
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1060
                                                {txt}Root MSE          =    {res} .63334

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1} ln_exp4_totaltime{col 20}{c |} Coefficient{col 32}  std. err.{col 44}      t{col 52}   P>|t|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
R2MVperformance_01 {c |}{col 20}{res}{space 2} .6255763{col 32}{space 2} .0610733{col 43}{space 1}   10.24{col 52}{space 3}0.000{col 60}{space 4} .5057669{col 73}{space 3} .7453857
{txt}{space 18} {c |}
{space 10}mvround2 {c |}
{space 1}Stadium Licenses  {c |}{col 20}{res}{space 2}-.1288958{col 32}{space 2} .0446215{col 43}{space 1}   -2.89{col 52}{space 3}0.004{col 60}{space 4}-.2164313{col 73}{space 3}-.0413604
{txt}Sulfur Reductions  {c |}{col 20}{res}{space 2} -.054226{col 32}{space 2} .0501253{col 43}{space 1}   -1.08{col 52}{space 3}0.280{col 60}{space 4}-.1525585{col 73}{space 3} .0441065
{txt}{space 1}Hazardous Plants  {c |}{col 20}{res}{space 2}  .010875{col 32}{space 2} .0448692{col 43}{space 1}    0.24{col 52}{space 3}0.809{col 60}{space 4}-.0771462{col 73}{space 3} .0988963
{txt}{space 18} {c |}
{space 13}_cons {c |}{col 20}{res}{space 2} 2.910141{col 32}{space 2} .0527425{col 43}{space 1}   55.18{col 52}{space 3}0.000{col 60}{space 4} 2.806674{col 73}{space 3} 3.013607
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         
. *Survey duration (70% increase)
. reg ln_durationinseconds R2MVperformance_01 i.mvround2 if expround2==4, robust 

{txt}Linear regression                               Number of obs     = {res}     1,356
                                                {txt}F(4, 1351)        =  {res}    31.12
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1054
                                                {txt}Root MSE          =    {res} .71721

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}ln_durationinsec~s{col 20}{c |} Coefficient{col 32}  std. err.{col 44}      t{col 52}   P>|t|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
R2MVperformance_01 {c |}{col 20}{res}{space 2} .7037689{col 32}{space 2} .0676103{col 43}{space 1}   10.41{col 52}{space 3}0.000{col 60}{space 4} .5711363{col 73}{space 3} .8364016
{txt}{space 18} {c |}
{space 10}mvround2 {c |}
{space 1}Stadium Licenses  {c |}{col 20}{res}{space 2}-.1487548{col 32}{space 2} .0540485{col 43}{space 1}   -2.75{col 52}{space 3}0.006{col 60}{space 4}-.2547828{col 73}{space 3}-.0427267
{txt}Sulfur Reductions  {c |}{col 20}{res}{space 2}-.1174943{col 32}{space 2} .0529746{col 43}{space 1}   -2.22{col 52}{space 3}0.027{col 60}{space 4}-.2214158{col 73}{space 3}-.0135728
{txt}{space 1}Hazardous Plants  {c |}{col 20}{res}{space 2} -.017241{col 32}{space 2} .0538294{col 43}{space 1}   -0.32{col 52}{space 3}0.749{col 60}{space 4}-.1228393{col 73}{space 3} .0883573
{txt}{space 18} {c |}
{space 13}_cons {c |}{col 20}{res}{space 2} 6.530856{col 32}{space 2} .0619655{col 43}{space 1}  105.40{col 52}{space 3}0.000{col 60}{space 4} 6.409297{col 73}{space 3} 6.652415
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Answering FMC correctly
. logit IMMIGexpFMC_Correct R2MVperformance_01 i.mvround2 if expround2==4

{res}{txt}Iteration 0:{space 3}log likelihood = {res: -685.5627}  
Iteration 1:{space 3}log likelihood = {res:-486.06535}  
Iteration 2:{space 3}log likelihood = {res:-464.89736}  
Iteration 3:{space 3}log likelihood = {res:-464.59198}  
Iteration 4:{space 3}log likelihood = {res:-464.59162}  
Iteration 5:{space 3}log likelihood = {res:-464.59162}  
{res}
{txt}{col 1}Logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:1,346}
{txt}{col 57}{lalign 13:LR chi2({res:4})}{col 70} = {res}{ralign 6:441.94}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 14:Log likelihood}{col 15} = {res}{ralign 10:-464.59162}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.3223}

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}IMMIGexpFMC_Correct{col 21}{c |} Coefficient{col 33}  Std. err.{col 45}      z{col 53}   P>|z|{col 61}     [95% con{col 74}f. interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}R2MVperformance_01 {c |}{col 21}{res}{space 2} 4.596097{col 33}{space 2} .2719422{col 44}{space 1}   16.90{col 53}{space 3}0.000{col 61}{space 4}   4.0631{col 74}{space 3} 5.129094
{txt}{space 19} {c |}
{space 11}mvround2 {c |}
{space 2}Stadium Licenses  {c |}{col 21}{res}{space 2}-.3851543{col 33}{space 2} .2247596{col 44}{space 1}   -1.71{col 53}{space 3}0.087{col 61}{space 4}-.8256751{col 74}{space 3} .0553664
{txt}{space 1}Sulfur Reductions  {c |}{col 21}{res}{space 2} .2016348{col 33}{space 2} .2313329{col 44}{space 1}    0.87{col 53}{space 3}0.383{col 61}{space 4}-.2517694{col 74}{space 3} .6550391
{txt}{space 2}Hazardous Plants  {c |}{col 21}{res}{space 2} .7076087{col 33}{space 2} .2463156{col 44}{space 1}    2.87{col 53}{space 3}0.004{col 61}{space 4}  .224839{col 74}{space 3} 1.190378
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2} -1.34034{col 33}{space 2} .2074047{col 44}{space 1}   -6.46{col 53}{space 3}0.000{col 61}{space 4}-1.746846{col 74}{space 3}-.9338343
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, dydx(R2MVperformance_01) // effect = .48
{res}
{txt}{col 1}Average marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:1,346}
{txt}{col 1}Model VCE: {res:OIM}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Pr(IMMIGexpFMC_Correct), predict()}{p_end}
{p2col:dy/dx wrt:}{res:R2MVperformance_01}{p_end}
{p2colreset}{...}

{res}{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32} Delta-method
{col 20}{c |}      dy/dx{col 32}   std. err.{col 44}      z{col 52}   P>|z|{col 60}     [95% con{col 73}f. interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
R2MVperformance_01 {c |}{col 20}{res}{space 2}  .484815{col 32}{space 2}  .016492{col 43}{space 1}   29.40{col 52}{space 3}0.000{col 60}{space 4} .4524913{col 73}{space 3} .5171388
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. **************
. *Appendix G
. **************
. 
. *Note: continue to use "Lucid1_replicationdata.dta"
. tab mvround1 // % randomly assigned to not receive an MV

             {txt}MVround1 {c |}      Freq.     Percent        Cum.
{hline 22}{c +}{hline 35}
                No MV {c |}{res}      1,179       20.02       20.02
{txt}Scientific Publishing {c |}{res}      1,178       20.00       40.02
{txt}     Stadium Licenses {c |}{res}      1,178       20.00       60.02
{txt}    Sulfur Reductions {c |}{res}      1,178       20.00       80.02
{txt}     Hazardous Plants {c |}{res}      1,177       19.98      100.00
{txt}{hline 22}{c +}{hline 35}
                Total {c |}{res}      5,890      100.00
{txt}
{com}. 
. use "Lucid1_AppendixG_SDtests_replicationdata.dta"
{txt}(Written by R.              )

{com}. 
. *Testing for significant variances in outcome by whether an MV is shown
. by EXP treatment, sort: sdtest outcome, by(MV_binary) // no signficant differences

{txt}{hline}
-> EXP = Student Loans, treatment = Control

Variance ratio test
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
   No MV {c |}{res}{col 12}    135{col 22} .0592014{col 34} .0853748{col 46} .9919657{col 58}-.1096551{col 70} .2280579
      {txt}MV {c |}{res}{col 12}  1,244{col 22}-.0064246{col 34} .0283823{col 46} 1.001053{col 58} -.062107{col 70} .0492578
{txt}{hline 9}{c +}{hline 68}
Combined {c |}{res}{col 12}  1,379{col 22}-1.23e-16{col 34} .0269289{col 46}        1{col 58} -.052826{col 70}  .052826
{txt}{hline 9}{c BT}{hline 68}
    ratio = sd({res}No MV{txt}) / sd({res}MV{txt})                                    f = {res}  0.9819
{txt}H0: ratio = 1                                   Degrees of freedom = {res}134, 1243

    {txt}Ha: ratio < 1               Ha: ratio != 1                 Ha: ratio > 1
  Pr(F < f) = {res}0.4574         {txt}2*Pr(F < f) = {res}0.9149        {txt}   Pr(F > f) = {res}0.5426

{txt}{hline}
-> EXP = Student Loans, treatment = Treatment

Variance ratio test
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
   No MV {c |}{res}{col 12}    133{col 22} .2321804{col 34} .0924642{col 46} 1.066349{col 58} .0492771{col 70} .4150837
      {txt}MV {c |}{res}{col 12}  1,243{col 22} .3987501{col 34} .0287118{col 46} 1.012268{col 58} .3424212{col 70} .4550791
{txt}{hline 9}{c +}{hline 68}
Combined {c |}{res}{col 12}  1,376{col 22}   .38265{col 34} .0274545{col 46}  1.01841{col 58} .3287928{col 70} .4365072
{txt}{hline 9}{c BT}{hline 68}
    ratio = sd({res}No MV{txt}) / sd({res}MV{txt})                                    f = {res}  1.1097
{txt}H0: ratio = 1                                   Degrees of freedom = {res}132, 1242

    {txt}Ha: ratio < 1               Ha: ratio != 1                 Ha: ratio > 1
  Pr(F < f) = {res}0.8023         {txt}2*Pr(F > f) = {res}0.3955        {txt}   Pr(F > f) = {res}0.1977

{txt}{hline}
-> EXP = KKK, treatment = Control

Variance ratio test
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
   No MV {c |}{res}{col 12}    138{col 22}-.0405501{col 34} .0831461{col 46}  .976745{col 58}-.2049657{col 70} .1238656
      {txt}MV {c |}{res}{col 12}  1,226{col 22} .0045644{col 34} .0286418{col 46} 1.002872{col 58} -.051628{col 70} .0607567
{txt}{hline 9}{c +}{hline 68}
Combined {c |}{res}{col 12}  1,364{col 22} 2.02e-17{col 34} .0270765{col 46}        1{col 58}-.0531162{col 70} .0531162
{txt}{hline 9}{c BT}{hline 68}
    ratio = sd({res}No MV{txt}) / sd({res}MV{txt})                                    f = {res}  0.9486
{txt}H0: ratio = 1                                   Degrees of freedom = {res}137, 1225

    {txt}Ha: ratio < 1               Ha: ratio != 1                 Ha: ratio > 1
  Pr(F < f) = {res}0.3530         {txt}2*Pr(F < f) = {res}0.7060        {txt}   Pr(F > f) = {res}0.6470

{txt}{hline}
-> EXP = KKK, treatment = Treatment

Variance ratio test
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
   No MV {c |}{res}{col 12}    144{col 22}  .448988{col 34} .0880262{col 46} 1.056314{col 58} .2749874{col 70} .6229887
      {txt}MV {c |}{res}{col 12}  1,221{col 22} .4099989{col 34} .0307311{col 46} 1.073831{col 58} .3497072{col 70} .4702906
{txt}{hline 9}{c +}{hline 68}
Combined {c |}{res}{col 12}  1,365{col 22} .4141121{col 34} .0290068{col 46} 1.071681{col 58} .3572094{col 70} .4710147
{txt}{hline 9}{c BT}{hline 68}
    ratio = sd({res}No MV{txt}) / sd({res}MV{txt})                                    f = {res}  0.9676
{txt}H0: ratio = 1                                   Degrees of freedom = {res}143, 1220

    {txt}Ha: ratio < 1               Ha: ratio != 1                 Ha: ratio > 1
  Pr(F < f) = {res}0.4095         {txt}2*Pr(F < f) = {res}0.8189        {txt}   Pr(F > f) = {res}0.5905

{txt}{hline}
-> EXP = Welfare, treatment = Control

Variance ratio test
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
   No MV {c |}{res}{col 12}    146{col 22}-.0285011{col 34} .0822206{col 46} .9934759{col 58}-.1910069{col 70} .1340047
      {txt}MV {c |}{res}{col 12}  1,221{col 22}  .003408{col 34} .0286505{col 46} 1.001128{col 58}-.0528016{col 70} .0596176
{txt}{hline 9}{c +}{hline 68}
Combined {c |}{res}{col 12}  1,367{col 22} 1.38e-16{col 34} .0270468{col 46}        1{col 58}-.0530577{col 70} .0530577
{txt}{hline 9}{c BT}{hline 68}
    ratio = sd({res}No MV{txt}) / sd({res}MV{txt})                                    f = {res}  0.9848
{txt}H0: ratio = 1                                   Degrees of freedom = {res}145, 1220

    {txt}Ha: ratio < 1               Ha: ratio != 1                 Ha: ratio > 1
  Pr(F < f) = {res}0.4639         {txt}2*Pr(F < f) = {res}0.9278        {txt}   Pr(F > f) = {res}0.5361

{txt}{hline}
-> EXP = Welfare, treatment = Treatment

Variance ratio test
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
   No MV {c |}{res}{col 12}    158{col 22} 1.065858{col 34} .0800454{col 46} 1.006155{col 58} .9077529{col 70} 1.223962
      {txt}MV {c |}{res}{col 12}  1,217{col 22} 1.188743{col 34} .0275609{col 46} .9614778{col 58} 1.134671{col 70} 1.242816
{txt}{hline 9}{c +}{hline 68}
Combined {c |}{res}{col 12}  1,375{col 22} 1.174623{col 34} .0260817{col 46} .9671341{col 58} 1.123458{col 70} 1.225787
{txt}{hline 9}{c BT}{hline 68}
    ratio = sd({res}No MV{txt}) / sd({res}MV{txt})                                    f = {res}  1.0951
{txt}H0: ratio = 1                                   Degrees of freedom = {res}157, 1216

    {txt}Ha: ratio < 1               Ha: ratio != 1                 Ha: ratio > 1
  Pr(F < f) = {res}0.7872         {txt}2*Pr(F > f) = {res}0.4256        {txt}   Pr(F > f) = {res}0.2128

{txt}{hline}
-> EXP = Immigration, treatment = Control

Variance ratio test
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
   No MV {c |}{res}{col 12}    156{col 22}-.0449978{col 34} .0797528{col 46} .9961119{col 58}-.2025404{col 70} .1125448
      {txt}MV {c |}{res}{col 12}  1,214{col 22} .0057823{col 34} .0287224{col 46} 1.000761{col 58}-.0505689{col 70} .0621334
{txt}{hline 9}{c +}{hline 68}
Combined {c |}{res}{col 12}  1,370{col 22}-1.94e-18{col 34} .0270172{col 46}        1{col 58}-.0529995{col 70} .0529995
{txt}{hline 9}{c BT}{hline 68}
    ratio = sd({res}No MV{txt}) / sd({res}MV{txt})                                    f = {res}  0.9907
{txt}H0: ratio = 1                                   Degrees of freedom = {res}155, 1213

    {txt}Ha: ratio < 1               Ha: ratio != 1                 Ha: ratio > 1
  Pr(F < f) = {res}0.4817         {txt}2*Pr(F < f) = {res}0.9634        {txt}   Pr(F > f) = {res}0.5183

{txt}{hline}
-> EXP = Immigration, treatment = Treatment

Variance ratio test
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
   No MV {c |}{res}{col 12}    146{col 22} .1764738{col 34} .0806441{col 46} .9744269{col 58}  .017084{col 70} .3358637
      {txt}MV {c |}{res}{col 12}  1,227{col 22}  .357733{col 34} .0253888{col 46} .8893333{col 58} .3079227{col 70} .4075433
{txt}{hline 9}{c +}{hline 68}
Combined {c |}{res}{col 12}  1,373{col 22} .3384585{col 34} .0242922{col 46} .9001237{col 58} .2908046{col 70} .3861124
{txt}{hline 9}{c BT}{hline 68}
    ratio = sd({res}No MV{txt}) / sd({res}MV{txt})                                    f = {res}  1.2005
{txt}H0: ratio = 1                                   Degrees of freedom = {res}145, 1226

    {txt}Ha: ratio < 1               Ha: ratio != 1                 Ha: ratio > 1
  Pr(F < f) = {res}0.9381         {txt}2*Pr(F > f) = {res}0.1238        {txt}   Pr(F > f) = {res}0.0619

{txt}{hline}
-> EXP = ., treatment = .
no observations

{com}. 
. **************
. *Appendix D
. **************
. 
. *Demographic predictors of MVC performance
. use "Lucid1_TableD1_replicationdata.dta", clear
{txt}
{com}. 
. *Note: These data are in long-form (up to 2 observations per respondent)
. 
. *Table D1
. reg MVCscale01 female i.race_5cat age_01 income_01 educ_01 polint_01 pid7_01 ideology_01 ///
> i.ROUND i.Experiment, vce(cluster responseid_num) // model

{txt}Linear regression                               Number of obs     = {res}     9,900
                                                {txt}F(15, 5579)       =  {res}    66.71
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1177
                                                {txt}Root MSE          =    {res} .33212

{txt}{ralign 93:(Std. err. adjusted for {res:5,580} clusters in {res:responseid_num})}
{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}                 MVCscale01{col 29}{c |} Coefficient{col 41}  std. err.{col 53}      t{col 61}   P>|t|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}female {c |}{col 29}{res}{space 2} .0360038{col 41}{space 2} .0082247{col 52}{space 1}    4.38{col 61}{space 3}0.000{col 69}{space 4} .0198802{col 82}{space 3} .0521275
{txt}{space 27} {c |}
{space 18}race_5cat {c |}
Non-Hispanic African-Ame..  {c |}{col 29}{res}{space 2}-.0914662{col 41}{space 2}  .014753{col 52}{space 1}   -6.20{col 61}{space 3}0.000{col 69}{space 4}-.1203877{col 82}{space 3}-.0625446
{txt}{space 18}Hispanic  {c |}{col 29}{res}{space 2}-.0636279{col 41}{space 2}  .018014{col 52}{space 1}   -3.53{col 61}{space 3}0.000{col 69}{space 4}-.0989424{col 82}{space 3}-.0283135
{txt}{space 21}Asian  {c |}{col 29}{res}{space 2}-.0553502{col 41}{space 2} .0230524{col 52}{space 1}   -2.40{col 61}{space 3}0.016{col 69}{space 4}-.1005419{col 82}{space 3}-.0101585
{txt}{space 21}Other  {c |}{col 29}{res}{space 2}-.0363948{col 41}{space 2} .0223603{col 52}{space 1}   -1.63{col 61}{space 3}0.104{col 69}{space 4}-.0802297{col 82}{space 3} .0074402
{txt}{space 27} {c |}
{space 21}age_01 {c |}{col 29}{res}{space 2} .4759778{col 41}{space 2}   .02063{col 52}{space 1}   23.07{col 61}{space 3}0.000{col 69}{space 4}  .435535{col 82}{space 3} .5164206
{txt}{space 18}income_01 {c |}{col 29}{res}{space 2}-.0415897{col 41}{space 2} .0174026{col 52}{space 1}   -2.39{col 61}{space 3}0.017{col 69}{space 4}-.0757056{col 82}{space 3}-.0074738
{txt}{space 20}educ_01 {c |}{col 29}{res}{space 2}   .04698{col 41}{space 2} .0214353{col 52}{space 1}    2.19{col 61}{space 3}0.028{col 69}{space 4} .0049585{col 82}{space 3} .0890015
{txt}{space 18}polint_01 {c |}{col 29}{res}{space 2} .0720008{col 41}{space 2} .0179228{col 52}{space 1}    4.02{col 61}{space 3}0.000{col 69}{space 4} .0368652{col 82}{space 3} .1071364
{txt}{space 20}pid7_01 {c |}{col 29}{res}{space 2}-.0551547{col 41}{space 2} .0176172{col 52}{space 1}   -3.13{col 61}{space 3}0.002{col 69}{space 4}-.0896912{col 82}{space 3}-.0206182
{txt}{space 16}ideology_01 {c |}{col 29}{res}{space 2} .0137465{col 41}{space 2} .0209226{col 52}{space 1}    0.66{col 61}{space 3}0.511{col 69}{space 4}  -.02727{col 82}{space 3}  .054763
{txt}{space 20}2.ROUND {c |}{col 29}{res}{space 2} .0338147{col 41}{space 2} .0049446{col 52}{space 1}    6.84{col 61}{space 3}0.000{col 69}{space 4} .0241214{col 82}{space 3} .0435081
{txt}{space 27} {c |}
{space 17}Experiment {c |}
{space 15}KKK Framing  {c |}{col 29}{res}{space 2} .0072688{col 41}{space 2} .0086249{col 52}{space 1}    0.84{col 61}{space 3}0.399{col 69}{space 4}-.0096393{col 82}{space 3} .0241769
{txt}{space 19}Welfare  {c |}{col 29}{res}{space 2} .0001901{col 41}{space 2} .0087793{col 52}{space 1}    0.02{col 61}{space 3}0.983{col 69}{space 4}-.0170208{col 82}{space 3}  .017401
{txt}{space 15}Immigration  {c |}{col 29}{res}{space 2}-.0006778{col 41}{space 2} .0086814{col 52}{space 1}   -0.08{col 61}{space 3}0.938{col 69}{space 4}-.0176967{col 82}{space 3}  .016341
{txt}{space 27} {c |}
{space 22}_cons {c |}{col 29}{res}{space 2} .4548882{col 41}{space 2} .0185324{col 52}{space 1}   24.55{col 61}{space 3}0.000{col 69}{space 4} .4185576{col 82}{space 3} .4912189
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
.         *Add Above Model to Table D2
. outreg2 using TableD1.doc, append ctitle(Lucid) dec(2) e(r2_a) ///
> alpha(0.001, 0.01, 0.05, .10) symbol(***, **, *,^) 
{txt}{stata `"shellout using `"TableD1.doc"'"':TableD1.doc}
{browse `"/Users/JohnVKane/Desktop/Analyze Attentive R&R/Replication Files/PSRM STATA Replication Files"' :dir}{com} : {txt}{stata `"seeout using "TableD1.txt""':seeout}

{com}. 
. *********************
. *********************
. *LUCID STUDY 2
. *********************
. *********************
. 
. use "Lucid2_Appendix_B_and_G_replicationdata.dta", clear
{txt}
{com}. 
. ********************
. *Table B1. Demographic Results 
. ********************
. 
. tab gender // n size reported

     {txt}gender {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       Male {c |}{res}      4,462       48.78       48.78
{txt}     Female {c |}{res}      4,686       51.22      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      9,148      100.00
{txt}
{com}. 
. *Descriptive statistics for income, education, age and political interest
. tabstat income educ age polint, st(mean p50)

{txt}   Stats {...}
{c |}{...}
    income      educ       age    polint
{hline 9}{c +}{hline 40}
{ralign 8:Mean} {...}
{c |}{...}
 {res} 2.836202  3.248249  48.41266  3.256178
{txt}{ralign 8:p50} {...}
{c |}{...}
 {res}        2         3        49         3
{txt}{hline 9}{c BT}{hline 40}

{com}. 
. tab gender // % female

     {txt}gender {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       Male {c |}{res}      4,462       48.78       48.78
{txt}     Female {c |}{res}      4,686       51.22      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      9,148      100.00
{txt}
{com}. tab ethnicity_clean // % in each racial group

  {txt}RECODE of {c |}
  ethnicity {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
      White {c |}{res}      6,777       74.08       74.08
{txt}      Black {c |}{res}      1,087       11.88       85.96
{txt}      Other {c |}{res}      1,042       11.39       97.35
{txt}     Refuse {c |}{res}        242        2.65      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      9,148      100.00
{txt}
{com}. tab Hispanic_clean // % identifying as Hispanic

  {txt}RECODE of {c |}
   hispanic {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
         No {c |}{res}      7,723       84.42       84.42
{txt}        Yes {c |}{res}      1,150       12.57       96.99
{txt}     Refuse {c |}{res}        275        3.01      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      9,148      100.00
{txt}
{com}. tab pid7 // % in each partisan category

             {txt}pid7 {c |}      Freq.     Percent        Cum.
{hline 18}{c +}{hline 35}
  Strong Democrat {c |}{res}      1,599       17.54       17.54
{txt}         Democrat {c |}{res}      1,462       16.04       33.57
{txt}    Lean Democrat {c |}{res}        986       10.81       44.39
{txt}      Independent {c |}{res}      1,860       20.40       64.79
{txt}  Lean Republican {c |}{res}        962       10.55       75.34
{txt}       Republican {c |}{res}      1,078       11.82       87.17
{txt}Strong Republican {c |}{res}      1,170       12.83      100.00
{txt}{hline 18}{c +}{hline 35}
            Total {c |}{res}      9,117      100.00
{txt}
{com}. tab ideology // % in each ideological category

              {txt}ideology {c |}      Freq.     Percent        Cum.
{hline 23}{c +}{hline 35}
     Extremely Liberal {c |}{res}        789        8.64        8.64
{txt}               Liberal {c |}{res}      1,318       14.44       23.09
{txt}      Slightly Liberal {c |}{res}        847        9.28       32.37
{txt}              Moderate {c |}{res}      3,038       33.29       65.65
{txt} Slightly Conservative {c |}{res}        870        9.53       75.18
{txt}          Conservative {c |}{res}      1,376       15.08       90.26
{txt}Extremely Conservative {c |}{res}        889        9.74      100.00
{txt}{hline 23}{c +}{hline 35}
                 Total {c |}{res}      9,127      100.00
{txt}
{com}. 
. 
. *Additional information
. codebook income 

{txt}{hline}
{res}income{right:(unlabeled)}
{txt}{hline}

{col 19}Type: Numeric ({res}byte{txt})
{ralign 22:Label}: {res:income}

{col 18}Range: [{res}1{txt},{res}7{txt}]{col 55}Units: {res}1
{col 10}{txt}Unique values: {res}7{col 51}{txt}Missing .: {res}27{txt}/{res}9,148

{txt}{col 13}Tabulation: Freq.   Numeric  Label
{col 20}{res}     2,215{col 32}       1{col 42}{txt}0-25k
{col 20}{res}     2,443{col 32}       2{col 42}{txt}25k-50k
{col 20}{res}     1,648{col 32}       3{col 42}{txt}50k-75k
{col 20}{res}     1,120{col 32}       4{col 42}{txt}75-100k
{col 20}{res}     1,078{col 32}       5{col 42}{txt}100k-150k
{col 20}{res}       365{col 32}       6{col 42}{txt}150-200k
{col 20}{res}       252{col 32}       7{col 42}{txt}Over 200k
{col 20}{res}        27{col 32}       .{col 42}
{txt}
{com}. codebook educ 

{txt}{hline}
{res}educ{right:(unlabeled)}
{txt}{hline}

{col 19}Type: Numeric ({res}byte{txt})
{ralign 22:Label}: {res:educ}

{col 18}Range: [{res}1{txt},{res}6{txt}]{col 55}Units: {res}1
{col 10}{txt}Unique values: {res}6{col 51}{txt}Missing .: {res}8{txt}/{res}9,148

{txt}{col 13}Tabulation: Freq.   Numeric  Label
{col 20}{res}       330{col 32}       1{col 42}{txt}<HS
{col 20}{res}     2,223{col 32}       2{col 42}{txt}HS grad
{col 20}{res}     2,842{col 32}       3{col 42}{txt}Some college
{col 20}{res}     2,570{col 32}       4{col 42}{txt}College grad
{col 20}{res}       943{col 32}       5{col 42}{txt}Masters
{col 20}{res}       232{col 32}       6{col 42}{txt}PhD or other advanced
{col 20}{res}         8{col 32}       .{col 42}
{txt}
{com}. codebook polint 

{txt}{hline}
{res}polint{right:(unlabeled)}
{txt}{hline}

{col 19}Type: Numeric ({res}byte{txt})
{ralign 22:Label}: {res:polint}, but {res:3} nonmissing values are not labeled

{col 18}Range: [{res}1{txt},{res}5{txt}]{col 55}Units: {res}1
{col 10}{txt}Unique values: {res}5{col 51}{txt}Missing .: {res}2{txt}/{res}9,148

{txt}{col 13}Tabulation: Freq.   Numeric  Label
{col 20}{res}       868{col 32}       1{col 42}{txt}Not interested
{col 20}{res}     1,664{col 32}       2{col 42}
{col 20}     2,537{col 32}       3{col 42}
{col 20}     2,411{col 32}       4{col 42}
{col 20}     1,666{col 32}       5{col 42}{txt}Extremely interested
{col 20}{res}         2{col 32}       .{col 42}
{txt}
{com}. 
. *Appendix G
.         * % of sample randomly assigned to not receive an MV
. tab checkround // checkround=0 indicates respondents had no MV in first round

 {txt}CheckRound {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,063       11.62       11.62
{txt}          1 {c |}{res}      1,031       11.27       22.89
{txt}          2 {c |}{res}      1,026       11.22       34.11
{txt}          3 {c |}{res}      1,041       11.38       45.49
{txt}          4 {c |}{res}      1,009       11.03       56.52
{txt}          5 {c |}{res}        976       10.67       67.18
{txt}          6 {c |}{res}      1,005       10.99       78.17
{txt}          7 {c |}{res}        982       10.73       88.90
{txt}          8 {c |}{res}      1,015       11.10      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      9,148      100.00
{txt}
{com}. 
. 
. ******************************************
. ******************************************
. 
. log close // close log
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/Users/JohnVKane/Desktop/Analyze Attentive R&R/Replication Files/PSRM STATA Replication Files/PSRM_Replication_Stata_Analyses.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res} 6 Dec 2022, 23:48:42
{txt}{.-}
{smcl}
{txt}{sf}{ul off}